FastMath dist3

Percentage Accurate: 98.0% → 99.9%

Time: 5.0s

Alternatives: 8

Speedup: 1.9×

• 21.4% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (+ (+ (* d1 d2) (* (+ d3 5.0) d1)) (* d1 32.0)))

C

double code(double d1, double d2, double d3) {
	return ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0);
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    code = ((d1 * d2) + ((d3 + 5.0d0) * d1)) + (d1 * 32.0d0)
end function

Java

public static double code(double d1, double d2, double d3) {
	return ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0);
}

Python

def code(d1, d2, d3):
	return ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0)

Julia

function code(d1, d2, d3)
	return Float64(Float64(Float64(d1 * d2) + Float64(Float64(d3 + 5.0) * d1)) + Float64(d1 * 32.0))
end

MATLAB

function tmp = code(d1, d2, d3)
	tmp = ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0);
end

Wolfram

code[d1_, d2_, d3_] := N[(N[(N[(d1 * d2), $MachinePrecision] + N[(N[(d3 + 5.0), $MachinePrecision] * d1), $MachinePrecision]), $MachinePrecision] + N[(d1 * 32.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 98.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (+ (+ (* d1 d2) (* (+ d3 5.0) d1)) (* d1 32.0)))

C

double code(double d1, double d2, double d3) {
	return ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0);
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    code = ((d1 * d2) + ((d3 + 5.0d0) * d1)) + (d1 * 32.0d0)
end function

Java

public static double code(double d1, double d2, double d3) {
	return ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0);
}

Python

def code(d1, d2, d3):
	return ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0)

Julia

function code(d1, d2, d3)
	return Float64(Float64(Float64(d1 * d2) + Float64(Float64(d3 + 5.0) * d1)) + Float64(d1 * 32.0))
end

MATLAB

function tmp = code(d1, d2, d3)
	tmp = ((d1 * d2) + ((d3 + 5.0) * d1)) + (d1 * 32.0);
end

Wolfram

code[d1_, d2_, d3_] := N[(N[(N[(d1 * d2), $MachinePrecision] + N[(N[(d3 + 5.0), $MachinePrecision] * d1), $MachinePrecision]), $MachinePrecision] + N[(d1 * 32.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ d1 \cdot \left(d3 + d2\right) + d1 \cdot 37 \end{array} \]

FPCore

(FPCore (d1 d2 d3) :precision binary64 (+ (* d1 (+ d3 d2)) (* d1 37.0)))

C

double code(double d1, double d2, double d3) {
	return (d1 * (d3 + d2)) + (d1 * 37.0);
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    code = (d1 * (d3 + d2)) + (d1 * 37.0d0)
end function

Java

public static double code(double d1, double d2, double d3) {
	return (d1 * (d3 + d2)) + (d1 * 37.0);
}

Python

def code(d1, d2, d3):
	return (d1 * (d3 + d2)) + (d1 * 37.0)

Julia

function code(d1, d2, d3)
	return Float64(Float64(d1 * Float64(d3 + d2)) + Float64(d1 * 37.0))
end

MATLAB

function tmp = code(d1, d2, d3)
	tmp = (d1 * (d3 + d2)) + (d1 * 37.0);
end

Wolfram

code[d1_, d2_, d3_] := N[(N[(d1 * N[(d3 + d2), $MachinePrecision]), $MachinePrecision] + N[(d1 * 37.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.2%

   \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
2. Step-by-step derivation

   1. cancel-sign-sub 97.2%

      \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

   2. +-commutative 97.2%

      \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

   3. *-commutative 97.2%

      \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

   4. distribute-lft-out 99.9%

      \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

   5. distribute-lft-neg-out 99.9%

      \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

   6. distribute-rgt-neg-in 99.9%

      \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

   7. distribute-lft-out-- 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

   8. associate-+r+ 100.0%

      \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

   9. +-commutative 100.0%

      \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

   10. associate--l+ 100.0%

       \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

   11. sub-neg 100.0%

       \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

   12. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

   13. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

   14. associate-+l+ 100.0%

       \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

   15. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. associate-+r+ 100.0%

      \[\leadsto d1 \cdot \color{blue}{\left(\left(d3 + d2\right) + 37\right)} \]

   2. distribute-rgt-in 100.0%

      \[\leadsto \color{blue}{\left(d3 + d2\right) \cdot d1 + 37 \cdot d1} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\left(d3 + d2\right) \cdot d1 + 37 \cdot d1} \]

7. Final simplification 100.0%

   \[\leadsto d1 \cdot \left(d3 + d2\right) + d1 \cdot 37 \]
8. Add Preprocessing

Alternative 2: 62.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;d2 \leq -37 \lor \neg \left(d2 \leq 36\right):\\ \;\;\;\;d2 \cdot d1\\ \mathbf{else}:\\ \;\;\;\;d1 \cdot 37\\ \end{array} \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (if (or (<= d2 -37.0) (not (<= d2 36.0))) (* d2 d1) (* d1 37.0)))

C

double code(double d1, double d2, double d3) {
	double tmp;
	if ((d2 <= -37.0) || !(d2 <= 36.0)) {
		tmp = d2 * d1;
	} else {
		tmp = d1 * 37.0;
	}
	return tmp;
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    real(8) :: tmp
    if ((d2 <= (-37.0d0)) .or. (.not. (d2 <= 36.0d0))) then
        tmp = d2 * d1
    else
        tmp = d1 * 37.0d0
    end if
    code = tmp
end function

Java

public static double code(double d1, double d2, double d3) {
	double tmp;
	if ((d2 <= -37.0) || !(d2 <= 36.0)) {
		tmp = d2 * d1;
	} else {
		tmp = d1 * 37.0;
	}
	return tmp;
}

Python

def code(d1, d2, d3):
	tmp = 0
	if (d2 <= -37.0) or not (d2 <= 36.0):
		tmp = d2 * d1
	else:
		tmp = d1 * 37.0
	return tmp

Julia

function code(d1, d2, d3)
	tmp = 0.0
	if ((d2 <= -37.0) || !(d2 <= 36.0))
		tmp = Float64(d2 * d1);
	else
		tmp = Float64(d1 * 37.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(d1, d2, d3)
	tmp = 0.0;
	if ((d2 <= -37.0) || ~((d2 <= 36.0)))
		tmp = d2 * d1;
	else
		tmp = d1 * 37.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[d1_, d2_, d3_] := If[Or[LessEqual[d2, -37.0], N[Not[LessEqual[d2, 36.0]], $MachinePrecision]], N[(d2 * d1), $MachinePrecision], N[(d1 * 37.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if d2 < -37 or 36 < d2

   1. Initial program 94.5%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 94.5%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 94.5%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 94.5%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d2 around inf 80.9%

      \[\leadsto \color{blue}{d1 \cdot d2} \]

   if -37 < d2 < 36

   1. Initial program 99.9%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 99.9%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 99.9%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 99.9%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around 0 55.5%

      \[\leadsto \color{blue}{d1 \cdot \left(37 + d2\right)} \]

   6. Taylor expanded in d2 around 0 54.5%

      \[\leadsto \color{blue}{37 \cdot d1} \]
   7. Step-by-step derivation

      1. *-commutative 54.5%

         \[\leadsto \color{blue}{d1 \cdot 37} \]

   8. Simplified 54.5%

      \[\leadsto \color{blue}{d1 \cdot 37} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;d2 \leq -37 \lor \neg \left(d2 \leq 36\right):\\ \;\;\;\;d2 \cdot d1\\ \mathbf{else}:\\ \;\;\;\;d1 \cdot 37\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 51.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;d2 \leq -37:\\ \;\;\;\;d2 \cdot d1\\ \mathbf{elif}\;d2 \leq -5.8 \cdot 10^{-288}:\\ \;\;\;\;d1 \cdot 37\\ \mathbf{else}:\\ \;\;\;\;d3 \cdot d1\\ \end{array} \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (if (<= d2 -37.0) (* d2 d1) (if (<= d2 -5.8e-288) (* d1 37.0) (* d3 d1))))

C

double code(double d1, double d2, double d3) {
	double tmp;
	if (d2 <= -37.0) {
		tmp = d2 * d1;
	} else if (d2 <= -5.8e-288) {
		tmp = d1 * 37.0;
	} else {
		tmp = d3 * d1;
	}
	return tmp;
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    real(8) :: tmp
    if (d2 <= (-37.0d0)) then
        tmp = d2 * d1
    else if (d2 <= (-5.8d-288)) then
        tmp = d1 * 37.0d0
    else
        tmp = d3 * d1
    end if
    code = tmp
end function

Java

public static double code(double d1, double d2, double d3) {
	double tmp;
	if (d2 <= -37.0) {
		tmp = d2 * d1;
	} else if (d2 <= -5.8e-288) {
		tmp = d1 * 37.0;
	} else {
		tmp = d3 * d1;
	}
	return tmp;
}

Python

def code(d1, d2, d3):
	tmp = 0
	if d2 <= -37.0:
		tmp = d2 * d1
	elif d2 <= -5.8e-288:
		tmp = d1 * 37.0
	else:
		tmp = d3 * d1
	return tmp

Julia

function code(d1, d2, d3)
	tmp = 0.0
	if (d2 <= -37.0)
		tmp = Float64(d2 * d1);
	elseif (d2 <= -5.8e-288)
		tmp = Float64(d1 * 37.0);
	else
		tmp = Float64(d3 * d1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(d1, d2, d3)
	tmp = 0.0;
	if (d2 <= -37.0)
		tmp = d2 * d1;
	elseif (d2 <= -5.8e-288)
		tmp = d1 * 37.0;
	else
		tmp = d3 * d1;
	end
	tmp_2 = tmp;
end

Wolfram

code[d1_, d2_, d3_] := If[LessEqual[d2, -37.0], N[(d2 * d1), $MachinePrecision], If[LessEqual[d2, -5.8e-288], N[(d1 * 37.0), $MachinePrecision], N[(d3 * d1), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if d2 < -37

   1. Initial program 95.8%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 95.8%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 95.8%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 95.8%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 99.9%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 99.9%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 99.9%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 99.9%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d2 around inf 82.0%

      \[\leadsto \color{blue}{d1 \cdot d2} \]

   if -37 < d2 < -5.8000000000000003e-288

   1. Initial program 99.9%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 99.9%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 99.9%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 99.9%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 99.9%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 99.9%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 99.9%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 99.9%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around 0 63.6%

      \[\leadsto \color{blue}{d1 \cdot \left(37 + d2\right)} \]

   6. Taylor expanded in d2 around 0 62.3%

      \[\leadsto \color{blue}{37 \cdot d1} \]
   7. Step-by-step derivation

      1. *-commutative 62.3%

         \[\leadsto \color{blue}{d1 \cdot 37} \]

   8. Simplified 62.3%

      \[\leadsto \color{blue}{d1 \cdot 37} \]

   if -5.8000000000000003e-288 < d2

   1. Initial program 96.6%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 96.6%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 96.6%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 96.6%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around inf 42.4%

      \[\leadsto \color{blue}{d1 \cdot d3} \]
3. Recombined 3 regimes into one program.

4. Final simplification 58.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;d2 \leq -37:\\ \;\;\;\;d2 \cdot d1\\ \mathbf{elif}\;d2 \leq -5.8 \cdot 10^{-288}:\\ \;\;\;\;d1 \cdot 37\\ \mathbf{else}:\\ \;\;\;\;d3 \cdot d1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 80.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;d3 \leq 10:\\ \;\;\;\;d1 \cdot \left(d2 + 37\right)\\ \mathbf{else}:\\ \;\;\;\;d1 \cdot \left(d3 + d2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (if (<= d3 10.0) (* d1 (+ d2 37.0)) (* d1 (+ d3 d2))))

C

double code(double d1, double d2, double d3) {
	double tmp;
	if (d3 <= 10.0) {
		tmp = d1 * (d2 + 37.0);
	} else {
		tmp = d1 * (d3 + d2);
	}
	return tmp;
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    real(8) :: tmp
    if (d3 <= 10.0d0) then
        tmp = d1 * (d2 + 37.0d0)
    else
        tmp = d1 * (d3 + d2)
    end if
    code = tmp
end function

Java

public static double code(double d1, double d2, double d3) {
	double tmp;
	if (d3 <= 10.0) {
		tmp = d1 * (d2 + 37.0);
	} else {
		tmp = d1 * (d3 + d2);
	}
	return tmp;
}

Python

def code(d1, d2, d3):
	tmp = 0
	if d3 <= 10.0:
		tmp = d1 * (d2 + 37.0)
	else:
		tmp = d1 * (d3 + d2)
	return tmp

Julia

function code(d1, d2, d3)
	tmp = 0.0
	if (d3 <= 10.0)
		tmp = Float64(d1 * Float64(d2 + 37.0));
	else
		tmp = Float64(d1 * Float64(d3 + d2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(d1, d2, d3)
	tmp = 0.0;
	if (d3 <= 10.0)
		tmp = d1 * (d2 + 37.0);
	else
		tmp = d1 * (d3 + d2);
	end
	tmp_2 = tmp;
end

Wolfram

code[d1_, d2_, d3_] := If[LessEqual[d3, 10.0], N[(d1 * N[(d2 + 37.0), $MachinePrecision]), $MachinePrecision], N[(d1 * N[(d3 + d2), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if d3 < 10

   1. Initial program 97.9%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 97.9%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 97.9%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 97.9%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around 0 76.9%

      \[\leadsto \color{blue}{d1 \cdot \left(37 + d2\right)} \]

   if 10 < d3

   1. Initial program 95.0%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. associate-+l+ 95.0%

         \[\leadsto \color{blue}{d1 \cdot d2 + \left(\left(d3 + 5\right) \cdot d1 + d1 \cdot 32\right)} \]

      2. *-commutative 95.0%

         \[\leadsto \color{blue}{d2 \cdot d1} + \left(\left(d3 + 5\right) \cdot d1 + d1 \cdot 32\right) \]

      3. fma-define 96.7%

         \[\leadsto \color{blue}{\mathsf{fma}\left(d2, d1, \left(d3 + 5\right) \cdot d1 + d1 \cdot 32\right)} \]

      4. *-commutative 96.7%

         \[\leadsto \mathsf{fma}\left(d2, d1, \color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot 32\right) \]

      5. distribute-lft-out 96.7%

         \[\leadsto \mathsf{fma}\left(d2, d1, \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + 32\right)}\right) \]

   4. Applied egg-rr 96.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(d2, d1, d1 \cdot \left(\left(d3 + 5\right) + 32\right)\right)} \]

   5. Taylor expanded in d3 around inf 96.3%

      \[\leadsto \mathsf{fma}\left(d2, d1, d1 \cdot \color{blue}{d3}\right) \]

   6. Taylor expanded in d2 around 0 94.6%

      \[\leadsto \color{blue}{d1 \cdot d2 + d1 \cdot d3} \]
   7. Step-by-step derivation

      1. distribute-lft-in 99.6%

         \[\leadsto \color{blue}{d1 \cdot \left(d2 + d3\right)} \]

   8. Simplified 99.6%

      \[\leadsto \color{blue}{d1 \cdot \left(d2 + d3\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 82.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;d3 \leq 10:\\ \;\;\;\;d1 \cdot \left(d2 + 37\right)\\ \mathbf{else}:\\ \;\;\;\;d1 \cdot \left(d3 + d2\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 76.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;d2 \leq -1.7 \cdot 10^{-6}:\\ \;\;\;\;d1 \cdot \left(d2 + 37\right)\\ \mathbf{else}:\\ \;\;\;\;d1 \cdot \left(d3 + 37\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (if (<= d2 -1.7e-6) (* d1 (+ d2 37.0)) (* d1 (+ d3 37.0))))

C

double code(double d1, double d2, double d3) {
	double tmp;
	if (d2 <= -1.7e-6) {
		tmp = d1 * (d2 + 37.0);
	} else {
		tmp = d1 * (d3 + 37.0);
	}
	return tmp;
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    real(8) :: tmp
    if (d2 <= (-1.7d-6)) then
        tmp = d1 * (d2 + 37.0d0)
    else
        tmp = d1 * (d3 + 37.0d0)
    end if
    code = tmp
end function

Java

public static double code(double d1, double d2, double d3) {
	double tmp;
	if (d2 <= -1.7e-6) {
		tmp = d1 * (d2 + 37.0);
	} else {
		tmp = d1 * (d3 + 37.0);
	}
	return tmp;
}

Python

def code(d1, d2, d3):
	tmp = 0
	if d2 <= -1.7e-6:
		tmp = d1 * (d2 + 37.0)
	else:
		tmp = d1 * (d3 + 37.0)
	return tmp

Julia

function code(d1, d2, d3)
	tmp = 0.0
	if (d2 <= -1.7e-6)
		tmp = Float64(d1 * Float64(d2 + 37.0));
	else
		tmp = Float64(d1 * Float64(d3 + 37.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(d1, d2, d3)
	tmp = 0.0;
	if (d2 <= -1.7e-6)
		tmp = d1 * (d2 + 37.0);
	else
		tmp = d1 * (d3 + 37.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[d1_, d2_, d3_] := If[LessEqual[d2, -1.7e-6], N[(d1 * N[(d2 + 37.0), $MachinePrecision]), $MachinePrecision], N[(d1 * N[(d3 + 37.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if d2 < -1.70000000000000003e-6

   1. Initial program 95.9%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 95.9%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 95.9%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 95.9%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 99.9%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 99.9%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 99.9%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 99.9%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 99.9%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around 0 84.0%

      \[\leadsto \color{blue}{d1 \cdot \left(37 + d2\right)} \]

   if -1.70000000000000003e-6 < d2

   1. Initial program 97.7%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 97.7%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 97.7%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 97.7%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d2 around 0 77.9%

      \[\leadsto \color{blue}{d1 \cdot \left(37 + d3\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;d2 \leq -1.7 \cdot 10^{-6}:\\ \;\;\;\;d1 \cdot \left(d2 + 37\right)\\ \mathbf{else}:\\ \;\;\;\;d1 \cdot \left(d3 + 37\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 75.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;d3 \leq 1050000000:\\ \;\;\;\;d1 \cdot \left(d2 + 37\right)\\ \mathbf{else}:\\ \;\;\;\;d3 \cdot d1\\ \end{array} \end{array} \]

FPCore

(FPCore (d1 d2 d3)
 :precision binary64
 (if (<= d3 1050000000.0) (* d1 (+ d2 37.0)) (* d3 d1)))

C

double code(double d1, double d2, double d3) {
	double tmp;
	if (d3 <= 1050000000.0) {
		tmp = d1 * (d2 + 37.0);
	} else {
		tmp = d3 * d1;
	}
	return tmp;
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    real(8) :: tmp
    if (d3 <= 1050000000.0d0) then
        tmp = d1 * (d2 + 37.0d0)
    else
        tmp = d3 * d1
    end if
    code = tmp
end function

Java

public static double code(double d1, double d2, double d3) {
	double tmp;
	if (d3 <= 1050000000.0) {
		tmp = d1 * (d2 + 37.0);
	} else {
		tmp = d3 * d1;
	}
	return tmp;
}

Python

def code(d1, d2, d3):
	tmp = 0
	if d3 <= 1050000000.0:
		tmp = d1 * (d2 + 37.0)
	else:
		tmp = d3 * d1
	return tmp

Julia

function code(d1, d2, d3)
	tmp = 0.0
	if (d3 <= 1050000000.0)
		tmp = Float64(d1 * Float64(d2 + 37.0));
	else
		tmp = Float64(d3 * d1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(d1, d2, d3)
	tmp = 0.0;
	if (d3 <= 1050000000.0)
		tmp = d1 * (d2 + 37.0);
	else
		tmp = d3 * d1;
	end
	tmp_2 = tmp;
end

Wolfram

code[d1_, d2_, d3_] := If[LessEqual[d3, 1050000000.0], N[(d1 * N[(d2 + 37.0), $MachinePrecision]), $MachinePrecision], N[(d3 * d1), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if d3 < 1.05e9

   1. Initial program 97.9%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 97.9%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 97.9%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 97.9%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 99.9%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 99.9%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around 0 77.3%

      \[\leadsto \color{blue}{d1 \cdot \left(37 + d2\right)} \]

   if 1.05e9 < d3

   1. Initial program 94.7%

      \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
   2. Step-by-step derivation

      1. cancel-sign-sub 94.7%

         \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

      2. +-commutative 94.7%

         \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

      3. *-commutative 94.7%

         \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

      4. distribute-lft-out 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

      5. distribute-lft-neg-out 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

      6. distribute-rgt-neg-in 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

      7. distribute-lft-out-- 100.0%

         \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

      8. associate-+r+ 100.0%

         \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

      9. +-commutative 100.0%

         \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

      10. associate--l+ 100.0%

          \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

      11. sub-neg 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

      12. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

      13. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

      14. associate-+l+ 100.0%

          \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

      15. metadata-eval 100.0%

          \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in d3 around inf 73.4%

      \[\leadsto \color{blue}{d1 \cdot d3} \]
3. Recombined 2 regimes into one program.

4. Final simplification 76.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;d3 \leq 1050000000:\\ \;\;\;\;d1 \cdot \left(d2 + 37\right)\\ \mathbf{else}:\\ \;\;\;\;d3 \cdot d1\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 100.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ d1 \cdot \left(d3 + \left(d2 + 37\right)\right) \end{array} \]

FPCore

(FPCore (d1 d2 d3) :precision binary64 (* d1 (+ d3 (+ d2 37.0))))

C

double code(double d1, double d2, double d3) {
	return d1 * (d3 + (d2 + 37.0));
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    code = d1 * (d3 + (d2 + 37.0d0))
end function

Java

public static double code(double d1, double d2, double d3) {
	return d1 * (d3 + (d2 + 37.0));
}

Python

def code(d1, d2, d3):
	return d1 * (d3 + (d2 + 37.0))

Julia

function code(d1, d2, d3)
	return Float64(d1 * Float64(d3 + Float64(d2 + 37.0)))
end

MATLAB

function tmp = code(d1, d2, d3)
	tmp = d1 * (d3 + (d2 + 37.0));
end

Wolfram

code[d1_, d2_, d3_] := N[(d1 * N[(d3 + N[(d2 + 37.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.2%

   \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
2. Step-by-step derivation

   1. cancel-sign-sub 97.2%

      \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

   2. +-commutative 97.2%

      \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

   3. *-commutative 97.2%

      \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

   4. distribute-lft-out 99.9%

      \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

   5. distribute-lft-neg-out 99.9%

      \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

   6. distribute-rgt-neg-in 99.9%

      \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

   7. distribute-lft-out-- 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

   8. associate-+r+ 100.0%

      \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

   9. +-commutative 100.0%

      \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

   10. associate--l+ 100.0%

       \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

   11. sub-neg 100.0%

       \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

   12. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

   13. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

   14. associate-+l+ 100.0%

       \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

   15. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 8: 26.3% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ d1 \cdot 37 \end{array} \]

FPCore

(FPCore (d1 d2 d3) :precision binary64 (* d1 37.0))

C

double code(double d1, double d2, double d3) {
	return d1 * 37.0;
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    code = d1 * 37.0d0
end function

Java

public static double code(double d1, double d2, double d3) {
	return d1 * 37.0;
}

Python

def code(d1, d2, d3):
	return d1 * 37.0

Julia

function code(d1, d2, d3)
	return Float64(d1 * 37.0)
end

MATLAB

function tmp = code(d1, d2, d3)
	tmp = d1 * 37.0;
end

Wolfram

code[d1_, d2_, d3_] := N[(d1 * 37.0), $MachinePrecision]

Derivation

1. Initial program 97.2%

   \[\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) + d1 \cdot 32 \]
2. Step-by-step derivation

   1. cancel-sign-sub 97.2%

      \[\leadsto \color{blue}{\left(d1 \cdot d2 + \left(d3 + 5\right) \cdot d1\right) - \left(-d1\right) \cdot 32} \]

   2. +-commutative 97.2%

      \[\leadsto \color{blue}{\left(\left(d3 + 5\right) \cdot d1 + d1 \cdot d2\right)} - \left(-d1\right) \cdot 32 \]

   3. *-commutative 97.2%

      \[\leadsto \left(\color{blue}{d1 \cdot \left(d3 + 5\right)} + d1 \cdot d2\right) - \left(-d1\right) \cdot 32 \]

   4. distribute-lft-out 99.9%

      \[\leadsto \color{blue}{d1 \cdot \left(\left(d3 + 5\right) + d2\right)} - \left(-d1\right) \cdot 32 \]

   5. distribute-lft-neg-out 99.9%

      \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{\left(-d1 \cdot 32\right)} \]

   6. distribute-rgt-neg-in 99.9%

      \[\leadsto d1 \cdot \left(\left(d3 + 5\right) + d2\right) - \color{blue}{d1 \cdot \left(-32\right)} \]

   7. distribute-lft-out-- 100.0%

      \[\leadsto \color{blue}{d1 \cdot \left(\left(\left(d3 + 5\right) + d2\right) - \left(-32\right)\right)} \]

   8. associate-+r+ 100.0%

      \[\leadsto d1 \cdot \left(\color{blue}{\left(d3 + \left(5 + d2\right)\right)} - \left(-32\right)\right) \]

   9. +-commutative 100.0%

      \[\leadsto d1 \cdot \left(\left(d3 + \color{blue}{\left(d2 + 5\right)}\right) - \left(-32\right)\right) \]

   10. associate--l+ 100.0%

       \[\leadsto d1 \cdot \color{blue}{\left(d3 + \left(\left(d2 + 5\right) - \left(-32\right)\right)\right)} \]

   11. sub-neg 100.0%

       \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(\left(d2 + 5\right) + \left(-\left(-32\right)\right)\right)}\right) \]

   12. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \left(-\color{blue}{-32}\right)\right)\right) \]

   13. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(\left(d2 + 5\right) + \color{blue}{32}\right)\right) \]

   14. associate-+l+ 100.0%

       \[\leadsto d1 \cdot \left(d3 + \color{blue}{\left(d2 + \left(5 + 32\right)\right)}\right) \]

   15. metadata-eval 100.0%

       \[\leadsto d1 \cdot \left(d3 + \left(d2 + \color{blue}{37}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{d1 \cdot \left(d3 + \left(d2 + 37\right)\right)} \]
4. Add Preprocessing

5. Taylor expanded in d3 around 0 69.0%

   \[\leadsto \color{blue}{d1 \cdot \left(37 + d2\right)} \]

6. Taylor expanded in d2 around 0 28.6%

   \[\leadsto \color{blue}{37 \cdot d1} \]
7. Step-by-step derivation

   1. *-commutative 28.6%

      \[\leadsto \color{blue}{d1 \cdot 37} \]

8. Simplified 28.6%

   \[\leadsto \color{blue}{d1 \cdot 37} \]
9. Add Preprocessing

Developer target: 100.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ d1 \cdot \left(\left(37 + d3\right) + d2\right) \end{array} \]

FPCore

(FPCore (d1 d2 d3) :precision binary64 (* d1 (+ (+ 37.0 d3) d2)))

C

double code(double d1, double d2, double d3) {
	return d1 * ((37.0 + d3) + d2);
}

Fortran

real(8) function code(d1, d2, d3)
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8), intent (in) :: d3
    code = d1 * ((37.0d0 + d3) + d2)
end function

Java

public static double code(double d1, double d2, double d3) {
	return d1 * ((37.0 + d3) + d2);
}

Python

def code(d1, d2, d3):
	return d1 * ((37.0 + d3) + d2)

Julia

function code(d1, d2, d3)
	return Float64(d1 * Float64(Float64(37.0 + d3) + d2))
end

MATLAB

function tmp = code(d1, d2, d3)
	tmp = d1 * ((37.0 + d3) + d2);
end

Wolfram

code[d1_, d2_, d3_] := N[(d1 * N[(N[(37.0 + d3), $MachinePrecision] + d2), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024100 
(FPCore (d1 d2 d3)
  :name "FastMath dist3"
  :precision binary64

  :alt
  (* d1 (+ (+ 37.0 d3) d2))

  (+ (+ (* d1 d2) (* (+ d3 5.0) d1)) (* d1 32.0)))