Curve intersection, scale width based on ribbon orientation

Percentage Accurate: 97.1% → 98.6%

Time: 12.4s

Alternatives: 14

Speedup: 38.3×

Specification

\[\left(\left(\left(0 \leq normAngle \land normAngle \leq \frac{\mathsf{PI}\left(\right)}{2}\right) \land \left(-1 \leq n0\_i \land n0\_i \leq 1\right)\right) \land \left(-1 \leq n1\_i \land n1\_i \leq 1\right)\right) \land \left(2.328306437 \cdot 10^{-10} \leq u \land u \leq 1\right)\]

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{\sin normAngle}\\ \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot t\_0\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot t\_0\right) \cdot n1\_i \end{array} \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (let* ((t_0 (/ 1.0 (sin normAngle))))
   (+
    (* (* (sin (* (- 1.0 u) normAngle)) t_0) n0_i)
    (* (* (sin (* u normAngle)) t_0) n1_i))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	float t_0 = 1.0f / sinf(normAngle);
	return ((sinf(((1.0f - u) * normAngle)) * t_0) * n0_i) + ((sinf((u * normAngle)) * t_0) * n1_i);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(4) function code(normangle, u, n0_i, n1_i)
use fmin_fmax_functions
    real(4), intent (in) :: normangle
    real(4), intent (in) :: u
    real(4), intent (in) :: n0_i
    real(4), intent (in) :: n1_i
    real(4) :: t_0
    t_0 = 1.0e0 / sin(normangle)
    code = ((sin(((1.0e0 - u) * normangle)) * t_0) * n0_i) + ((sin((u * normangle)) * t_0) * n1_i)
end function

Julia

function code(normAngle, u, n0_i, n1_i)
	t_0 = Float32(Float32(1.0) / sin(normAngle))
	return Float32(Float32(Float32(sin(Float32(Float32(Float32(1.0) - u) * normAngle)) * t_0) * n0_i) + Float32(Float32(sin(Float32(u * normAngle)) * t_0) * n1_i))
end

MATLAB

function tmp = code(normAngle, u, n0_i, n1_i)
	t_0 = single(1.0) / sin(normAngle);
	tmp = ((sin(((single(1.0) - u) * normAngle)) * t_0) * n0_i) + ((sin((u * normAngle)) * t_0) * n1_i);
end

Initial Program: 97.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{\sin normAngle}\\ \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot t\_0\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot t\_0\right) \cdot n1\_i \end{array} \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (let* ((t_0 (/ 1.0 (sin normAngle))))
   (+
    (* (* (sin (* (- 1.0 u) normAngle)) t_0) n0_i)
    (* (* (sin (* u normAngle)) t_0) n1_i))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	float t_0 = 1.0f / sinf(normAngle);
	return ((sinf(((1.0f - u) * normAngle)) * t_0) * n0_i) + ((sinf((u * normAngle)) * t_0) * n1_i);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(4) function code(normangle, u, n0_i, n1_i)
use fmin_fmax_functions
    real(4), intent (in) :: normangle
    real(4), intent (in) :: u
    real(4), intent (in) :: n0_i
    real(4), intent (in) :: n1_i
    real(4) :: t_0
    t_0 = 1.0e0 / sin(normangle)
    code = ((sin(((1.0e0 - u) * normangle)) * t_0) * n0_i) + ((sin((u * normangle)) * t_0) * n1_i)
end function

Julia

function code(normAngle, u, n0_i, n1_i)
	t_0 = Float32(Float32(1.0) / sin(normAngle))
	return Float32(Float32(Float32(sin(Float32(Float32(Float32(1.0) - u) * normAngle)) * t_0) * n0_i) + Float32(Float32(sin(Float32(u * normAngle)) * t_0) * n1_i))
end

MATLAB

function tmp = code(normAngle, u, n0_i, n1_i)
	t_0 = single(1.0) / sin(normAngle);
	tmp = ((sin(((single(1.0) - u) * normAngle)) * t_0) * n0_i) + ((sin((u * normAngle)) * t_0) * n1_i);
end

Alternative 1: 98.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, n1\_i \cdot \left(\frac{u}{\sin normAngle} \cdot normAngle\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (fma
  (* (sin (* normAngle (- 1.0 u))) (/ 1.0 (sin normAngle)))
  n0_i
  (* n1_i (* (/ u (sin normAngle)) normAngle))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf((sinf((normAngle * (1.0f - u))) * (1.0f / sinf(normAngle))), n0_i, (n1_i * ((u / sinf(normAngle)) * normAngle)));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(Float32(sin(Float32(normAngle * Float32(Float32(1.0) - u))) * Float32(Float32(1.0) / sin(normAngle))), n0_i, Float32(n1_i * Float32(Float32(u / sin(normAngle)) * normAngle)))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]
6. Step-by-step derivation

   1. lower-fma.f32 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right)} \]

   2. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\sin \color{blue}{\left(normAngle \cdot \left(1 - u\right)\right)} \cdot \frac{1}{\sin normAngle}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   3. lower-*.f32 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   4. lower-sin.f32 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sin \left(normAngle \cdot \left(1 - u\right)\right)} \cdot \frac{1}{\sin normAngle}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   5. lower-*.f32 N/A

      \[\leadsto \mathsf{fma}\left(\sin \color{blue}{\left(normAngle \cdot \left(1 - u\right)\right)} \cdot \frac{1}{\sin normAngle}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   6. lower--.f32 N/A

      \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \color{blue}{\left(1 - u\right)}\right) \cdot \frac{1}{\sin normAngle}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   7. lower-/.f32 N/A

      \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \color{blue}{\frac{1}{\sin normAngle}}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   8. lower-sin.f32 N/A

      \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\color{blue}{\sin normAngle}}, n0\_i, \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i\right) \]

   9. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, \color{blue}{n1\_i \cdot \left(normAngle \cdot \frac{u}{\sin normAngle}\right)}\right) \]

   10. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, \color{blue}{n1\_i \cdot \left(normAngle \cdot \frac{u}{\sin normAngle}\right)}\right) \]

   11. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, n1\_i \cdot \left(\frac{u}{\sin normAngle} \cdot \color{blue}{normAngle}\right)\right) \]

   12. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, n1\_i \cdot \left(\frac{u}{\sin normAngle} \cdot \color{blue}{normAngle}\right)\right) \]

   13. lower-/.f32 N/A

       \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, n1\_i \cdot \left(\frac{u}{\sin normAngle} \cdot normAngle\right)\right) \]

   14. lower-sin.f32 99.0

       \[\leadsto \mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, n1\_i \cdot \left(\frac{u}{\sin normAngle} \cdot normAngle\right)\right) \]

7. Applied rewrites 99.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\sin \left(normAngle \cdot \left(1 - u\right)\right) \cdot \frac{1}{\sin normAngle}, n0\_i, n1\_i \cdot \left(\frac{u}{\sin normAngle} \cdot normAngle\right)\right)} \]
8. Add Preprocessing

Alternative 2: 99.0% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ n0\_i + \mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \mathsf{fma}\left(u, \mathsf{fma}\left(-0.5, n0\_i \cdot \left(normAngle \cdot normAngle\right), -0.16666666666666666 \cdot \left(\left(normAngle \cdot normAngle\right) \cdot \left(u \cdot \left(n1\_i - n0\_i\right)\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i - n0\_i, 0.5 \cdot n0\_i\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (+
  n0_i
  (fma
   u
   (+
    n1_i
    (fma
     -1.0
     n0_i
     (fma
      u
      (fma
       -0.5
       (* n0_i (* normAngle normAngle))
       (*
        -0.16666666666666666
        (* (* normAngle normAngle) (* u (- n1_i n0_i)))))
      (*
       (* normAngle normAngle)
       (fma 0.16666666666666666 (- n1_i n0_i) (* 0.5 n0_i))))))
   (*
    (* normAngle normAngle)
    (fma -0.16666666666666666 n0_i (* 0.16666666666666666 n0_i))))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return n0_i + fmaf(u, (n1_i + fmaf(-1.0f, n0_i, fmaf(u, fmaf(-0.5f, (n0_i * (normAngle * normAngle)), (-0.16666666666666666f * ((normAngle * normAngle) * (u * (n1_i - n0_i))))), ((normAngle * normAngle) * fmaf(0.16666666666666666f, (n1_i - n0_i), (0.5f * n0_i)))))), ((normAngle * normAngle) * fmaf(-0.16666666666666666f, n0_i, (0.16666666666666666f * n0_i))));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return Float32(n0_i + fma(u, Float32(n1_i + fma(Float32(-1.0), n0_i, fma(u, fma(Float32(-0.5), Float32(n0_i * Float32(normAngle * normAngle)), Float32(Float32(-0.16666666666666666) * Float32(Float32(normAngle * normAngle) * Float32(u * Float32(n1_i - n0_i))))), Float32(Float32(normAngle * normAngle) * fma(Float32(0.16666666666666666), Float32(n1_i - n0_i), Float32(Float32(0.5) * n0_i)))))), Float32(Float32(normAngle * normAngle) * fma(Float32(-0.16666666666666666), n0_i, Float32(Float32(0.16666666666666666) * n0_i)))))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \left(\frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right)\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot u\right)\right)\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \color{blue}{\left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)}\right)\right) \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)\right)\right) \]

8. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, \mathsf{fma}\left({\left(1 - u\right)}^{3}, n0\_i, {u}^{3} \cdot n1\_i\right), 0.16666666666666666 \cdot \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right)} \]

9. Taylor expanded in u around 0

   \[\leadsto n0\_i + \color{blue}{\left(u \cdot \left(n1\_i + \left(-1 \cdot n0\_i + \left(u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot {normAngle}^{2}\right) + \frac{-1}{6} \cdot \left({normAngle}^{2} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right) + {normAngle}^{2} \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right) + {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \frac{1}{6} \cdot n0\_i\right)\right)} \]

11. Applied rewrites 99.0%

    \[\leadsto n0\_i + \color{blue}{\mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \mathsf{fma}\left(u, \mathsf{fma}\left(-0.5, n0\_i \cdot \left(normAngle \cdot normAngle\right), -0.16666666666666666 \cdot \left(\left(normAngle \cdot normAngle\right) \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right)} \]

12. Final simplification 99.0%

    \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \mathsf{fma}\left(u, \mathsf{fma}\left(-0.5, n0\_i \cdot \left(normAngle \cdot normAngle\right), -0.16666666666666666 \cdot \left(\left(normAngle \cdot normAngle\right) \cdot \left(u \cdot \left(n1\_i - n0\_i\right)\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i - n0\_i, 0.5 \cdot n0\_i\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right) \]
13. Add Preprocessing

Alternative 3: 99.0% accurate, 5.8× speedup

Math

\[\begin{array}{l} \\ n0\_i + \mathsf{fma}\left(u, n1\_i - n0\_i, \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i - n0\_i, \mathsf{fma}\left(0.5, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i, -0.16666666666666666 \cdot \left(u \cdot \left(n1\_i - n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (+
  n0_i
  (fma
   u
   (- n1_i n0_i)
   (*
    (* normAngle normAngle)
    (fma
     -0.16666666666666666
     n0_i
     (fma
      0.16666666666666666
      n0_i
      (*
       u
       (fma
        0.16666666666666666
        (- n1_i n0_i)
        (fma
         0.5
         n0_i
         (*
          u
          (fma
           -0.5
           n0_i
           (* -0.16666666666666666 (* u (- n1_i n0_i))))))))))))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return n0_i + fmaf(u, (n1_i - n0_i), ((normAngle * normAngle) * fmaf(-0.16666666666666666f, n0_i, fmaf(0.16666666666666666f, n0_i, (u * fmaf(0.16666666666666666f, (n1_i - n0_i), fmaf(0.5f, n0_i, (u * fmaf(-0.5f, n0_i, (-0.16666666666666666f * (u * (n1_i - n0_i))))))))))));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return Float32(n0_i + fma(u, Float32(n1_i - n0_i), Float32(Float32(normAngle * normAngle) * fma(Float32(-0.16666666666666666), n0_i, fma(Float32(0.16666666666666666), n0_i, Float32(u * fma(Float32(0.16666666666666666), Float32(n1_i - n0_i), fma(Float32(0.5), n0_i, Float32(u * fma(Float32(-0.5), n0_i, Float32(Float32(-0.16666666666666666) * Float32(u * Float32(n1_i - n0_i)))))))))))))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \left(\frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right)\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot u\right)\right)\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \color{blue}{\left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)}\right)\right) \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)\right)\right) \]

8. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, \mathsf{fma}\left({\left(1 - u\right)}^{3}, n0\_i, {u}^{3} \cdot n1\_i\right), 0.16666666666666666 \cdot \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right)} \]

9. Taylor expanded in u around 0

   \[\leadsto n0\_i + \color{blue}{\left(u \cdot \left(n1\_i + \left(-1 \cdot n0\_i + \left(u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot {normAngle}^{2}\right) + \frac{-1}{6} \cdot \left({normAngle}^{2} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right) + {normAngle}^{2} \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right) + {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \frac{1}{6} \cdot n0\_i\right)\right)} \]

11. Applied rewrites 99.0%

    \[\leadsto n0\_i + \color{blue}{\mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \mathsf{fma}\left(u, \mathsf{fma}\left(-0.5, n0\_i \cdot \left(normAngle \cdot normAngle\right), -0.16666666666666666 \cdot \left(\left(normAngle \cdot normAngle\right) \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right)} \]

12. Taylor expanded in normAngle around 0

    \[\leadsto n0\_i + \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right) + {normAngle}^{2} \cdot \color{blue}{\left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \left(\frac{1}{2} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot n0\_i + \frac{-1}{6} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right)\right)\right)\right)}\right) \]
13. Step-by-step derivation

    1. lower-fma.f32 N/A

       \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + -1 \cdot \color{blue}{n0\_i}, {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \left(\frac{1}{2} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot n0\_i + \frac{-1}{6} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \]

    2. lower-+.f32 N/A

       \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + -1 \cdot n0\_i, {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \left(\frac{1}{2} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot n0\_i + \frac{-1}{6} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \]

    3. lower-*.f32 N/A

       \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + -1 \cdot n0\_i, {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \left(\frac{1}{2} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot n0\_i + \frac{-1}{6} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \]

    4. lower-*.f32 N/A

       \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + -1 \cdot n0\_i, {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \left(\frac{1}{2} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot n0\_i + \frac{-1}{6} \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \]

14. Applied rewrites 99.0%

    \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + \color{blue}{-1 \cdot n0\_i}, \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, \mathsf{fma}\left(0.5, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i, -0.16666666666666666 \cdot \left(u \cdot \left(n1\_i + -1 \cdot n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \]

15. Final simplification 99.0%

    \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i - n0\_i, \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i - n0\_i, \mathsf{fma}\left(0.5, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i, -0.16666666666666666 \cdot \left(u \cdot \left(n1\_i - n0\_i\right)\right)\right)\right)\right)\right)\right)\right) \]
16. Add Preprocessing

Alternative 4: 98.9% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ n0\_i + \mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i - n0\_i, 0.5 \cdot n0\_i\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (+
  n0_i
  (fma
   u
   (+
    n1_i
    (fma
     -1.0
     n0_i
     (*
      (* normAngle normAngle)
      (fma 0.16666666666666666 (- n1_i n0_i) (* 0.5 n0_i)))))
   (*
    (* normAngle normAngle)
    (fma -0.16666666666666666 n0_i (* 0.16666666666666666 n0_i))))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return n0_i + fmaf(u, (n1_i + fmaf(-1.0f, n0_i, ((normAngle * normAngle) * fmaf(0.16666666666666666f, (n1_i - n0_i), (0.5f * n0_i))))), ((normAngle * normAngle) * fmaf(-0.16666666666666666f, n0_i, (0.16666666666666666f * n0_i))));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return Float32(n0_i + fma(u, Float32(n1_i + fma(Float32(-1.0), n0_i, Float32(Float32(normAngle * normAngle) * fma(Float32(0.16666666666666666), Float32(n1_i - n0_i), Float32(Float32(0.5) * n0_i))))), Float32(Float32(normAngle * normAngle) * fma(Float32(-0.16666666666666666), n0_i, Float32(Float32(0.16666666666666666) * n0_i)))))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \left(\frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right)\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot u\right)\right)\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \color{blue}{\left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)}\right)\right) \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)\right)\right) \]

8. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, \mathsf{fma}\left({\left(1 - u\right)}^{3}, n0\_i, {u}^{3} \cdot n1\_i\right), 0.16666666666666666 \cdot \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right)} \]

9. Taylor expanded in u around 0

   \[\leadsto n0\_i + \color{blue}{\left(u \cdot \left(n1\_i + \left(-1 \cdot n0\_i + {normAngle}^{2} \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right) + {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \frac{1}{6} \cdot n0\_i\right)\right)} \]
10. Step-by-step derivation

    1. lower-+.f32 N/A

       \[\leadsto n0\_i + \left(u \cdot \left(n1\_i + \left(-1 \cdot n0\_i + {normAngle}^{2} \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right) + \color{blue}{{normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \frac{1}{6} \cdot n0\_i\right)}\right) \]

    2. lower-fma.f32 N/A

       \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + \color{blue}{\left(-1 \cdot n0\_i + {normAngle}^{2} \cdot \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)}, {normAngle}^{2} \cdot \left(\frac{-1}{6} \cdot n0\_i + \frac{1}{6} \cdot n0\_i\right)\right) \]

11. Applied rewrites 98.8%

    \[\leadsto n0\_i + \color{blue}{\mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right)} \]

12. Final simplification 98.8%

    \[\leadsto n0\_i + \mathsf{fma}\left(u, n1\_i + \mathsf{fma}\left(-1, n0\_i, \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(0.16666666666666666, n1\_i - n0\_i, 0.5 \cdot n0\_i\right)\right), \left(normAngle \cdot normAngle\right) \cdot \mathsf{fma}\left(-0.16666666666666666, n0\_i, 0.16666666666666666 \cdot n0\_i\right)\right) \]
13. Add Preprocessing

Alternative 5: 98.7% accurate, 8.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(0.3333333333333333 + -0.5 \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (fma
  n0_i
  (- 1.0 u)
  (fma
   (fma
    0.16666666666666666
    (* n1_i u)
    (* n0_i (* u (+ 0.3333333333333333 (* -0.5 u)))))
   (* normAngle normAngle)
   (* n1_i u))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf(n0_i, (1.0f - u), fmaf(fmaf(0.16666666666666666f, (n1_i * u), (n0_i * (u * (0.3333333333333333f + (-0.5f * u))))), (normAngle * normAngle), (n1_i * u)));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(n0_i, Float32(Float32(1.0) - u), fma(fma(Float32(0.16666666666666666), Float32(n1_i * u), Float32(n0_i * Float32(u * Float32(Float32(0.3333333333333333) + Float32(Float32(-0.5) * u))))), Float32(normAngle * normAngle), Float32(n1_i * u)))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \left(\frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right)\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot u\right)\right)\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \color{blue}{\left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)}\right)\right) \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)\right)\right) \]

8. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, \mathsf{fma}\left({\left(1 - u\right)}^{3}, n0\_i, {u}^{3} \cdot n1\_i\right), 0.16666666666666666 \cdot \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right)} \]

9. Taylor expanded in u around 0

   \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
10. Step-by-step derivation

    1. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    2. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    3. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    4. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    5. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    6. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    7. lower-+.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    8. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    9. lower-*.f32 98.6

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i \cdot u, \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

11. Applied rewrites 98.6%

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i \cdot u, \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

12. Taylor expanded in n0_i around 0

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\frac{1}{6} \cdot \left(n1\_i \cdot u\right) + n0\_i \cdot \left(u \cdot \left(\frac{1}{3} + \frac{-1}{2} \cdot u\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
13. Step-by-step derivation

    1. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6}, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(\frac{1}{3} + \frac{-1}{2} \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    2. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6}, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(\frac{1}{3} + \frac{-1}{2} \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    3. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6}, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(\frac{1}{3} + \frac{-1}{2} \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    4. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6}, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(\frac{1}{3} + \frac{-1}{2} \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    5. lower-+.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6}, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(\frac{1}{3} + \frac{-1}{2} \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    6. lower-*.f32 98.6

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(0.3333333333333333 + -0.5 \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

14. Applied rewrites 98.6%

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, n1\_i \cdot u, n0\_i \cdot \left(u \cdot \left(0.3333333333333333 + -0.5 \cdot u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
15. Add Preprocessing

Alternative 6: 98.5% accurate, 9.6× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \left(0.16666666666666666 \cdot n1\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (fma
  n0_i
  (- 1.0 u)
  (fma
   (fma
    -0.16666666666666666
    n0_i
    (fma 0.16666666666666666 n0_i (* u (* 0.16666666666666666 n1_i))))
   (* normAngle normAngle)
   (* n1_i u))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf(n0_i, (1.0f - u), fmaf(fmaf(-0.16666666666666666f, n0_i, fmaf(0.16666666666666666f, n0_i, (u * (0.16666666666666666f * n1_i)))), (normAngle * normAngle), (n1_i * u)));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(n0_i, Float32(Float32(1.0) - u), fma(fma(Float32(-0.16666666666666666), n0_i, fma(Float32(0.16666666666666666), n0_i, Float32(u * Float32(Float32(0.16666666666666666) * n1_i)))), Float32(normAngle * normAngle), Float32(n1_i * u)))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \left(\frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right)\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot u\right)\right)\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \color{blue}{\left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)}\right)\right) \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)\right)\right) \]

8. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, \mathsf{fma}\left({\left(1 - u\right)}^{3}, n0\_i, {u}^{3} \cdot n1\_i\right), 0.16666666666666666 \cdot \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right)} \]

9. Taylor expanded in u around 0

   \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
10. Step-by-step derivation

    1. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    2. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    3. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    4. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    5. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    6. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    7. lower-+.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    8. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    9. lower-*.f32 98.6

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i \cdot u, \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

11. Applied rewrites 98.6%

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i \cdot u, \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

12. Taylor expanded in n0_i around 0

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{1}{6} \cdot n1\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
13. Step-by-step derivation

    1. lower-*.f32 98.3

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \left(0.16666666666666666 \cdot n1\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

14. Applied rewrites 98.3%

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \left(0.16666666666666666 \cdot n1\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
15. Add Preprocessing

Alternative 7: 98.5% accurate, 12.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(0.16666666666666666 \cdot \left(n1\_i \cdot u\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (fma
  n0_i
  (- 1.0 u)
  (fma (* 0.16666666666666666 (* n1_i u)) (* normAngle normAngle) (* n1_i u))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf(n0_i, (1.0f - u), fmaf((0.16666666666666666f * (n1_i * u)), (normAngle * normAngle), (n1_i * u)));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(n0_i, Float32(Float32(1.0) - u), fma(Float32(Float32(0.16666666666666666) * Float32(n1_i * u)), Float32(normAngle * normAngle), Float32(n1_i * u)))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\frac{normAngle \cdot u}{\sin normAngle}} \cdot n1\_i \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   2. lower-*.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \color{blue}{\frac{u}{\sin normAngle}}\right) \cdot n1\_i \]

   3. lower-/.f32 N/A

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\color{blue}{\sin normAngle}}\right) \cdot n1\_i \]

   4. lower-sin.f32 99.0

      \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(normAngle \cdot \frac{u}{\sin normAngle}\right) \cdot n1\_i \]

5. Applied rewrites 99.0%

   \[\leadsto \left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \color{blue}{\left(normAngle \cdot \frac{u}{\sin normAngle}\right)} \cdot n1\_i \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \left(\frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right)\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot u\right)\right)\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto n0\_i \cdot \left(1 - u\right) + \left(n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \color{blue}{\left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)}\right)\right) \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u + {normAngle}^{2} \cdot \left(\left(\frac{-1}{6} \cdot \left(n0\_i \cdot {\left(1 - u\right)}^{3}\right) + \frac{-1}{6} \cdot \left(n1\_i \cdot {u}^{3}\right)\right) - \frac{-1}{6} \cdot \left(n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u\right)\right)\right) \]

8. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, \mathsf{fma}\left({\left(1 - u\right)}^{3}, n0\_i, {u}^{3} \cdot n1\_i\right), 0.16666666666666666 \cdot \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right)} \]

9. Taylor expanded in u around 0

   \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\frac{-1}{6} \cdot n0\_i + \left(\frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
10. Step-by-step derivation

    1. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \frac{1}{6} \cdot n0\_i + u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    2. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    3. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \left(\frac{-1}{2} \cdot \left(n0\_i \cdot u\right) + \left(\frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    4. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    5. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \frac{1}{6} \cdot \left(n1\_i + -1 \cdot n0\_i\right) + \frac{1}{2} \cdot n0\_i\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    6. lower-fma.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    7. lower-+.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    8. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{6}, n0\_i, \mathsf{fma}\left(\frac{1}{6}, n0\_i, u \cdot \mathsf{fma}\left(\frac{-1}{2}, n0\_i \cdot u, \mathsf{fma}\left(\frac{1}{6}, n1\_i + -1 \cdot n0\_i, \frac{1}{2} \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    9. lower-*.f32 98.6

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i \cdot u, \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

11. Applied rewrites 98.6%

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, n0\_i, \mathsf{fma}\left(0.16666666666666666, n0\_i, u \cdot \mathsf{fma}\left(-0.5, n0\_i \cdot u, \mathsf{fma}\left(0.16666666666666666, n1\_i + -1 \cdot n0\_i, 0.5 \cdot n0\_i\right)\right)\right)\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

12. Taylor expanded in n0_i around 0

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\frac{1}{6} \cdot \left(n1\_i \cdot u\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
13. Step-by-step derivation

    1. lower-*.f32 N/A

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(\frac{1}{6} \cdot \left(n1\_i \cdot u\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

    2. lower-*.f32 98.3

       \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(0.16666666666666666 \cdot \left(n1\_i \cdot u\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]

14. Applied rewrites 98.3%

    \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, \mathsf{fma}\left(0.16666666666666666 \cdot \left(n1\_i \cdot u\right), normAngle \cdot normAngle, n1\_i \cdot u\right)\right) \]
15. Add Preprocessing

Alternative 8: 60.9% accurate, 25.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n0\_i \leq -1.0000000195414814 \cdot 10^{-24} \lor \neg \left(n0\_i \leq 1.9999999996399175 \cdot 10^{-23}\right):\\ \;\;\;\;n0\_i\\ \mathbf{else}:\\ \;\;\;\;n1\_i \cdot u\\ \end{array} \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (if (or (<= n0_i -1.0000000195414814e-24)
         (not (<= n0_i 1.9999999996399175e-23)))
   n0_i
   (* n1_i u)))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	float tmp;
	if ((n0_i <= -1.0000000195414814e-24f) || !(n0_i <= 1.9999999996399175e-23f)) {
		tmp = n0_i;
	} else {
		tmp = n1_i * u;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(4) function code(normangle, u, n0_i, n1_i)
use fmin_fmax_functions
    real(4), intent (in) :: normangle
    real(4), intent (in) :: u
    real(4), intent (in) :: n0_i
    real(4), intent (in) :: n1_i
    real(4) :: tmp
    if ((n0_i <= (-1.0000000195414814e-24)) .or. (.not. (n0_i <= 1.9999999996399175e-23))) then
        tmp = n0_i
    else
        tmp = n1_i * u
    end if
    code = tmp
end function

Julia

function code(normAngle, u, n0_i, n1_i)
	tmp = Float32(0.0)
	if ((n0_i <= Float32(-1.0000000195414814e-24)) || !(n0_i <= Float32(1.9999999996399175e-23)))
		tmp = n0_i;
	else
		tmp = Float32(n1_i * u);
	end
	return tmp
end

MATLAB

function tmp_2 = code(normAngle, u, n0_i, n1_i)
	tmp = single(0.0);
	if ((n0_i <= single(-1.0000000195414814e-24)) || ~((n0_i <= single(1.9999999996399175e-23))))
		tmp = n0_i;
	else
		tmp = n1_i * u;
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if n0_i < -1.00000002e-24 or 2e-23 < n0_i

   1. Initial program 98.5%

      \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
   2. Add Preprocessing

   3. Taylor expanded in u around 0

      \[\leadsto \color{blue}{n0\_i} \]
   4. Step-by-step derivation

   5. Applied rewrites 67.8%

      \[\leadsto \color{blue}{n0\_i} \]

   if -1.00000002e-24 < n0_i < 2e-23

   1. Initial program 97.9%

      \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
   2. Add Preprocessing

   3. Taylor expanded in normAngle around 0

      \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

      2. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

      3. lower-*.f32 N/A

         \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

      4. lower--.f32 95.3

         \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   5. Applied rewrites 95.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]

   6. Taylor expanded in n0_i around 0

      \[\leadsto n1\_i \cdot \color{blue}{u} \]
   7. Step-by-step derivation

      1. lower-*.f32 65.9

         \[\leadsto n1\_i \cdot u \]

   8. Applied rewrites 65.9%

      \[\leadsto n1\_i \cdot \color{blue}{u} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;n0\_i \leq -1.0000000195414814 \cdot 10^{-24} \lor \neg \left(n0\_i \leq 1.9999999996399175 \cdot 10^{-23}\right):\\ \;\;\;\;n0\_i\\ \mathbf{else}:\\ \;\;\;\;n1\_i \cdot u\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 83.0% accurate, 30.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n0\_i \leq 5.000000229068525 \cdot 10^{-19}:\\ \;\;\;\;\mathsf{fma}\left(n1\_i, u, n0\_i\right)\\ \mathbf{else}:\\ \;\;\;\;n0\_i \cdot \left(1 - u\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (if (<= n0_i 5.000000229068525e-19) (fma n1_i u n0_i) (* n0_i (- 1.0 u))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	float tmp;
	if (n0_i <= 5.000000229068525e-19f) {
		tmp = fmaf(n1_i, u, n0_i);
	} else {
		tmp = n0_i * (1.0f - u);
	}
	return tmp;
}

Julia

function code(normAngle, u, n0_i, n1_i)
	tmp = Float32(0.0)
	if (n0_i <= Float32(5.000000229068525e-19))
		tmp = fma(n1_i, u, n0_i);
	else
		tmp = Float32(n0_i * Float32(Float32(1.0) - u));
	end
	return tmp
end

Derivation

1. Split input into 2 regimes

2. if n0_i < 5.00000023e-19

   1. Initial program 98.2%

      \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
   2. Add Preprocessing

   3. Taylor expanded in normAngle around 0

      \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

      2. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

      3. lower-*.f32 N/A

         \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

      4. lower--.f32 96.9

         \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   5. Applied rewrites 96.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]

   6. Taylor expanded in u around 0

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 84.2%

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i\right) \]

   if 5.00000023e-19 < n0_i

   1. Initial program 98.6%

      \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
   2. Add Preprocessing

   3. Taylor expanded in normAngle around 0

      \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

      2. lower-fma.f32 N/A

         \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

      3. lower-*.f32 N/A

         \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

      4. lower--.f32 98.9

         \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   5. Applied rewrites 98.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]

   6. Taylor expanded in n0_i around inf

      \[\leadsto n0\_i \cdot \color{blue}{\left(1 - u\right)} \]
   7. Step-by-step derivation

      1. lower-*.f32 N/A

         \[\leadsto n0\_i \cdot \left(1 - \color{blue}{u}\right) \]

      2. lower--.f32 89.0

         \[\leadsto n0\_i \cdot \left(1 - u\right) \]

   8. Applied rewrites 89.0%

      \[\leadsto n0\_i \cdot \color{blue}{\left(1 - u\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 97.9% accurate, 30.6× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (fma n1_i u (* n0_i (- 1.0 u))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf(n1_i, u, (n0_i * (1.0f - u)));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(n1_i, u, Float32(n0_i * Float32(Float32(1.0) - u)))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

   3. lower-*.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   4. lower--.f32 97.4

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

5. Applied rewrites 97.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]
6. Add Preprocessing

Alternative 11: 97.8% accurate, 30.6× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(n0\_i, 1 - u, n1\_i \cdot u\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (fma n0_i (- 1.0 u) (* n1_i u)))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf(n0_i, (1.0f - u), (n1_i * u));
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(n0_i, Float32(Float32(1.0) - u), Float32(n1_i * u))
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

   3. lower-*.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   4. lower--.f32 97.4

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

5. Applied rewrites 97.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]

6. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
7. Step-by-step derivation

   1. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, \color{blue}{1 - u}, n1\_i \cdot u\right) \]

   2. lower--.f32 N/A

      \[\leadsto \mathsf{fma}\left(n0\_i, 1 - \color{blue}{u}, n1\_i \cdot u\right) \]

   3. lower-*.f32 97.3

      \[\leadsto \mathsf{fma}\left(n0\_i, 1 - u, n1\_i \cdot u\right) \]

8. Applied rewrites 97.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n0\_i, 1 - u, n1\_i \cdot u\right)} \]
9. Add Preprocessing

Alternative 12: 98.0% accurate, 38.3× speedup

Math

\[\begin{array}{l} \\ n0\_i + u \cdot \left(n1\_i - n0\_i\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (+ n0_i (* u (- n1_i n0_i))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return n0_i + (u * (n1_i - n0_i));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(4) function code(normangle, u, n0_i, n1_i)
use fmin_fmax_functions
    real(4), intent (in) :: normangle
    real(4), intent (in) :: u
    real(4), intent (in) :: n0_i
    real(4), intent (in) :: n1_i
    code = n0_i + (u * (n1_i - n0_i))
end function

Julia

function code(normAngle, u, n0_i, n1_i)
	return Float32(n0_i + Float32(u * Float32(n1_i - n0_i)))
end

MATLAB

function tmp = code(normAngle, u, n0_i, n1_i)
	tmp = n0_i + (u * (n1_i - n0_i));
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

   3. lower-*.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   4. lower--.f32 97.4

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

5. Applied rewrites 97.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]

6. Taylor expanded in u around 0

   \[\leadsto n0\_i + \color{blue}{u \cdot \left(n1\_i + -1 \cdot n0\_i\right)} \]
7. Step-by-step derivation

   1. lower-+.f32 N/A

      \[\leadsto n0\_i + u \cdot \color{blue}{\left(n1\_i + -1 \cdot n0\_i\right)} \]

   2. lower-*.f32 N/A

      \[\leadsto n0\_i + u \cdot \left(n1\_i + \color{blue}{-1 \cdot n0\_i}\right) \]

   3. lower-+.f32 N/A

      \[\leadsto n0\_i + u \cdot \left(n1\_i + -1 \cdot \color{blue}{n0\_i}\right) \]

   4. lower-*.f32 97.5

      \[\leadsto n0\_i + u \cdot \left(n1\_i + -1 \cdot n0\_i\right) \]

8. Applied rewrites 97.5%

   \[\leadsto n0\_i + \color{blue}{u \cdot \left(n1\_i + -1 \cdot n0\_i\right)} \]

9. Final simplification 97.5%

   \[\leadsto n0\_i + u \cdot \left(n1\_i - n0\_i\right) \]
10. Add Preprocessing

Alternative 13: 82.0% accurate, 65.6× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(n1\_i, u, n0\_i\right) \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i) :precision binary32 (fma n1_i u n0_i))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return fmaf(n1_i, u, n0_i);
}

Julia

function code(normAngle, u, n0_i, n1_i)
	return fma(n1_i, u, n0_i)
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in normAngle around 0

   \[\leadsto \color{blue}{n0\_i \cdot \left(1 - u\right) + n1\_i \cdot u} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto n1\_i \cdot u + \color{blue}{n0\_i \cdot \left(1 - u\right)} \]

   2. lower-fma.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, \color{blue}{u}, n0\_i \cdot \left(1 - u\right)\right) \]

   3. lower-*.f32 N/A

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

   4. lower--.f32 97.4

      \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right) \]

5. Applied rewrites 97.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(n1\_i, u, n0\_i \cdot \left(1 - u\right)\right)} \]

6. Taylor expanded in u around 0

   \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i\right) \]
7. Step-by-step derivation

8. Applied rewrites 82.0%

   \[\leadsto \mathsf{fma}\left(n1\_i, u, n0\_i\right) \]
9. Add Preprocessing

Alternative 14: 46.3% accurate, 459.0× speedup

Math

\[\begin{array}{l} \\ n0\_i \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i) :precision binary32 n0_i)

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	return n0_i;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(4) function code(normangle, u, n0_i, n1_i)
use fmin_fmax_functions
    real(4), intent (in) :: normangle
    real(4), intent (in) :: u
    real(4), intent (in) :: n0_i
    real(4), intent (in) :: n1_i
    code = n0_i
end function

Julia

function code(normAngle, u, n0_i, n1_i)
	return n0_i
end

MATLAB

function tmp = code(normAngle, u, n0_i, n1_i)
	tmp = n0_i;
end

Derivation

1. Initial program 98.3%

   \[\left(\sin \left(\left(1 - u\right) \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n0\_i + \left(\sin \left(u \cdot normAngle\right) \cdot \frac{1}{\sin normAngle}\right) \cdot n1\_i \]
2. Add Preprocessing

3. Taylor expanded in u around 0

   \[\leadsto \color{blue}{n0\_i} \]
4. Step-by-step derivation

5. Applied rewrites 53.1%

   \[\leadsto \color{blue}{n0\_i} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2025043 
(FPCore (normAngle u n0_i n1_i)
  :name "Curve intersection, scale width based on ribbon orientation"
  :precision binary32
  :pre (and (and (and (and (<= 0.0 normAngle) (<= normAngle (/ (PI) 2.0))) (and (<= -1.0 n0_i) (<= n0_i 1.0))) (and (<= -1.0 n1_i) (<= n1_i 1.0))) (and (<= 2.328306437e-10 u) (<= u 1.0)))
  (+ (* (* (sin (* (- 1.0 u) normAngle)) (/ 1.0 (sin normAngle))) n0_i) (* (* (sin (* u normAngle)) (/ 1.0 (sin normAngle))) n1_i)))