Curve intersection, scale width based on ribbon orientation

Percentage Accurate: 97.0% → 99.1%

Time: 6.2s

Alternatives: 7

Speedup: 45.9×

Specification

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

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: 97.0% 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: 99.1% accurate, 5.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in normAngle around 0

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

   1. *-commutative N/A

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

   2. lower-fma.f32 N/A

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

   3. lift--.f32 N/A

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

   4. +-commutative N/A

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

5. Applied rewrites 98.8%

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

6. Taylor expanded in u around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f32 N/A

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

8. Applied rewrites 99.2%

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

Alternative 2: 99.0% accurate, 7.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in normAngle around 0

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

   1. *-commutative N/A

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

   2. lower-fma.f32 N/A

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

   3. lift--.f32 N/A

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

   4. +-commutative N/A

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

5. Applied rewrites 98.8%

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

6. Taylor expanded in u around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f32 N/A

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

8. Applied rewrites 99.1%

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

Alternative 3: 98.9% accurate, 11.5× speedup

Math

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

FPCore

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

C

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

Julia

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in normAngle around 0

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

   1. *-commutative N/A

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

   2. lower-fma.f32 N/A

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

   3. lift--.f32 N/A

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

   4. +-commutative N/A

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

5. Applied rewrites 98.8%

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

6. Taylor expanded in u around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f32 N/A

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

8. Applied rewrites 99.0%

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

Alternative 4: 85.5% accurate, 21.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n1\_i \leq -1.9000000864326212 \cdot 10^{-24} \lor \neg \left(n1\_i \leq 4.999999841327613 \cdot 10^{-21}\right):\\ \;\;\;\;\mathsf{fma}\left(u, n1\_i, n0\_i\right)\\ \mathbf{else}:\\ \;\;\;\;n0\_i \cdot \left(1 - u\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (normAngle u n0_i n1_i)
 :precision binary32
 (if (or (<= n1_i -1.9000000864326212e-24)
         (not (<= n1_i 4.999999841327613e-21)))
   (fma u n1_i n0_i)
   (* n0_i (- 1.0 u))))

C

float code(float normAngle, float u, float n0_i, float n1_i) {
	float tmp;
	if ((n1_i <= -1.9000000864326212e-24f) || !(n1_i <= 4.999999841327613e-21f)) {
		tmp = fmaf(u, n1_i, 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 ((n1_i <= Float32(-1.9000000864326212e-24)) || !(n1_i <= Float32(4.999999841327613e-21)))
		tmp = fma(u, n1_i, 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 n1_i < -1.90000009e-24 or 4.99999984e-21 < n1_i

   1. Initial program 97.3%

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

   3. Taylor expanded in normAngle around 0

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

   5. Applied rewrites 96.6%

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

   6. Taylor expanded in u around 0

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

   8. Applied rewrites 88.3%

      \[\leadsto \color{blue}{n0\_i} + u \cdot n1\_i \]
   9. Step-by-step derivation

      1. lift-+.f32 N/A

         \[\leadsto \color{blue}{n0\_i + u \cdot n1\_i} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{u \cdot n1\_i + n0\_i} \]

      3. lift-*.f32 N/A

         \[\leadsto \color{blue}{u \cdot n1\_i} + n0\_i \]

      4. lower-fma.f32 88.4

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

   10. Applied rewrites 88.4%

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

   if -1.90000009e-24 < n1_i < 4.99999984e-21

   1. Initial program 97.1%

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

   3. Taylor expanded in n0_i around inf

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

      1. associate-/l* N/A

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

      2. lower-*.f32 N/A

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

      3. *-commutative N/A

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

      4. lower-/.f32 N/A

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

      5. lift-*.f32 N/A

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

      6. lift--.f32 N/A

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

      7. lift-sin.f32 N/A

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

      8. lift-sin.f32 93.4

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

   5. Applied rewrites 93.4%

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

   6. Taylor expanded in normAngle around 0

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

      1. lift--.f32 92.6

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

   8. Applied rewrites 92.6%

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

4. Final simplification 89.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;n1\_i \leq -1.9000000864326212 \cdot 10^{-24} \lor \neg \left(n1\_i \leq 4.999999841327613 \cdot 10^{-21}\right):\\ \;\;\;\;\mathsf{fma}\left(u, n1\_i, n0\_i\right)\\ \mathbf{else}:\\ \;\;\;\;n0\_i \cdot \left(1 - u\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.1% accurate, 45.9× speedup

Math

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

FPCore

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

C

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

Julia

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in normAngle around 0

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

   1. *-commutative N/A

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

   2. lower-fma.f32 N/A

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

   3. lift--.f32 N/A

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

   4. +-commutative N/A

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

5. Applied rewrites 98.8%

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

6. Taylor expanded in u around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f32 N/A

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

8. Applied rewrites 99.0%

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

9. Taylor expanded in normAngle around 0

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

    1. lower--.f32 98.0

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

11. Applied rewrites 98.0%

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

Alternative 6: 82.1% accurate, 65.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Derivation

1. Initial program 97.2%

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

3. Taylor expanded in normAngle around 0

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

5. Applied rewrites 96.9%

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

6. Taylor expanded in u around 0

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

8. Applied rewrites 81.6%

   \[\leadsto \color{blue}{n0\_i} + u \cdot n1\_i \]
9. Step-by-step derivation

   1. lift-+.f32 N/A

      \[\leadsto \color{blue}{n0\_i + u \cdot n1\_i} \]

   2. +-commutative N/A

      \[\leadsto \color{blue}{u \cdot n1\_i + n0\_i} \]

   3. lift-*.f32 N/A

      \[\leadsto \color{blue}{u \cdot n1\_i} + n0\_i \]

   4. lower-fma.f32 81.7

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

10. Applied rewrites 81.7%

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

Alternative 7: 46.7% 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 97.2%

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

3. Taylor expanded in u around 0

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

5. Applied rewrites 45.3%

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

Reproduce

herbie shell --seed 2025061 
(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)))