Expression, p6

Percentage Accurate: 94.4% → 100.0%

Time: 3.8s

Alternatives: 7

Speedup: 1.0×

• Unsound rule application detected in e-graph. Results may not be sound.

Specification

\[\left(\left(\left(-14 \leq a \land a \leq -13\right) \land \left(-3 \leq b \land b \leq -2\right)\right) \land \left(3 \leq c \land c \leq 3.5\right)\right) \land \left(12.5 \leq d \land d \leq 13.5\right)\]

Math

\[\begin{array}{l} \\ \left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* (+ a (+ b (+ c d))) 2.0))

C

double code(double a, double b, double c, double d) {
	return (a + (b + (c + d))) * 2.0;
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = (a + (b + (c + d))) * 2.0d0
end function

Java

public static double code(double a, double b, double c, double d) {
	return (a + (b + (c + d))) * 2.0;
}

Python

def code(a, b, c, d):
	return (a + (b + (c + d))) * 2.0

Julia

function code(a, b, c, d)
	return Float64(Float64(a + Float64(b + Float64(c + d))) * 2.0)
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = (a + (b + (c + d))) * 2.0;
end

Wolfram

code[a_, b_, c_, d_] := N[(N[(a + N[(b + N[(c + d), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]

Initial Program: 94.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* (+ a (+ b (+ c d))) 2.0))

C

double code(double a, double b, double c, double d) {
	return (a + (b + (c + d))) * 2.0;
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = (a + (b + (c + d))) * 2.0d0
end function

Java

public static double code(double a, double b, double c, double d) {
	return (a + (b + (c + d))) * 2.0;
}

Python

def code(a, b, c, d):
	return (a + (b + (c + d))) * 2.0

Julia

function code(a, b, c, d)
	return Float64(Float64(a + Float64(b + Float64(c + d))) * 2.0)
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = (a + (b + (c + d))) * 2.0;
end

Wolfram

code[a_, b_, c_, d_] := N[(N[(a + N[(b + N[(c + d), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(c + \left(\left(a + d\right) + b\right)\right) \cdot 2 \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* (+ c (+ (+ a d) b)) 2.0))

C

double code(double a, double b, double c, double d) {
	return (c + ((a + d) + b)) * 2.0;
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = (c + ((a + d) + b)) * 2.0d0
end function

Java

public static double code(double a, double b, double c, double d) {
	return (c + ((a + d) + b)) * 2.0;
}

Python

def code(a, b, c, d):
	return (c + ((a + d) + b)) * 2.0

Julia

function code(a, b, c, d)
	return Float64(Float64(c + Float64(Float64(a + d) + b)) * 2.0)
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = (c + ((a + d) + b)) * 2.0;
end

Wolfram

code[a_, b_, c_, d_] := N[(N[(c + N[(N[(a + d), $MachinePrecision] + b), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

2. Taylor expanded in a around 0 95.6%

   \[\leadsto \color{blue}{\left(c + \left(a + \left(d + b\right)\right)\right)} \cdot 2 \]
3. Step-by-step derivation

   1. associate-+r+ 100.0%

      \[\leadsto \left(c + \color{blue}{\left(\left(a + d\right) + b\right)}\right) \cdot 2 \]

   2. flip-+ 98.2%

      \[\leadsto \left(c + \color{blue}{\frac{\left(a + d\right) \cdot \left(a + d\right) - b \cdot b}{\left(a + d\right) - b}}\right) \cdot 2 \]

   3. +-commutative 98.2%

      \[\leadsto \left(c + \frac{\color{blue}{\left(d + a\right)} \cdot \left(a + d\right) - b \cdot b}{\left(a + d\right) - b}\right) \cdot 2 \]

   4. +-commutative 98.2%

      \[\leadsto \left(c + \frac{\left(d + a\right) \cdot \color{blue}{\left(d + a\right)} - b \cdot b}{\left(a + d\right) - b}\right) \cdot 2 \]

   5. fma-neg 98.1%

      \[\leadsto \left(c + \frac{\color{blue}{\mathsf{fma}\left(d + a, d + a, -b \cdot b\right)}}{\left(a + d\right) - b}\right) \cdot 2 \]

   6. +-commutative 98.1%

      \[\leadsto \left(c + \frac{\mathsf{fma}\left(\color{blue}{a + d}, d + a, -b \cdot b\right)}{\left(a + d\right) - b}\right) \cdot 2 \]

   7. +-commutative 98.1%

      \[\leadsto \left(c + \frac{\mathsf{fma}\left(a + d, \color{blue}{a + d}, -b \cdot b\right)}{\left(a + d\right) - b}\right) \cdot 2 \]

4. Applied egg-rr 98.1%

   \[\leadsto \left(c + \color{blue}{\frac{\mathsf{fma}\left(a + d, a + d, -b \cdot b\right)}{\left(a + d\right) - b}}\right) \cdot 2 \]
5. Step-by-step derivation

   1. fma-neg 98.2%

      \[\leadsto \left(c + \frac{\color{blue}{\left(a + d\right) \cdot \left(a + d\right) - b \cdot b}}{\left(a + d\right) - b}\right) \cdot 2 \]

   2. difference-of-squares 99.3%

      \[\leadsto \left(c + \frac{\color{blue}{\left(\left(a + d\right) + b\right) \cdot \left(\left(a + d\right) - b\right)}}{\left(a + d\right) - b}\right) \cdot 2 \]

   3. +-commutative 99.3%

      \[\leadsto \left(c + \frac{\left(\color{blue}{\left(d + a\right)} + b\right) \cdot \left(\left(a + d\right) - b\right)}{\left(a + d\right) - b}\right) \cdot 2 \]

   4. associate-+r+ 94.8%

      \[\leadsto \left(c + \frac{\color{blue}{\left(d + \left(a + b\right)\right)} \cdot \left(\left(a + d\right) - b\right)}{\left(a + d\right) - b}\right) \cdot 2 \]

   5. associate--l+ 94.1%

      \[\leadsto \left(c + \frac{\left(d + \left(a + b\right)\right) \cdot \color{blue}{\left(a + \left(d - b\right)\right)}}{\left(a + d\right) - b}\right) \cdot 2 \]

   6. +-commutative 94.1%

      \[\leadsto \left(c + \frac{\left(d + \left(a + b\right)\right) \cdot \color{blue}{\left(\left(d - b\right) + a\right)}}{\left(a + d\right) - b}\right) \cdot 2 \]

   7. associate-+l- 93.8%

      \[\leadsto \left(c + \frac{\left(d + \left(a + b\right)\right) \cdot \color{blue}{\left(d - \left(b - a\right)\right)}}{\left(a + d\right) - b}\right) \cdot 2 \]

   8. associate--l+ 94.5%

      \[\leadsto \left(c + \frac{\left(d + \left(a + b\right)\right) \cdot \left(d - \left(b - a\right)\right)}{\color{blue}{a + \left(d - b\right)}}\right) \cdot 2 \]

   9. +-commutative 94.5%

      \[\leadsto \left(c + \frac{\left(d + \left(a + b\right)\right) \cdot \left(d - \left(b - a\right)\right)}{\color{blue}{\left(d - b\right) + a}}\right) \cdot 2 \]

   10. associate-+l- 95.0%

       \[\leadsto \left(c + \frac{\left(d + \left(a + b\right)\right) \cdot \left(d - \left(b - a\right)\right)}{\color{blue}{d - \left(b - a\right)}}\right) \cdot 2 \]

6. Simplified 95.0%

   \[\leadsto \left(c + \color{blue}{\frac{\left(d + \left(a + b\right)\right) \cdot \left(d - \left(b - a\right)\right)}{d - \left(b - a\right)}}\right) \cdot 2 \]
7. Step-by-step derivation

   1. associate-/l* 95.0%

      \[\leadsto \left(c + \color{blue}{\frac{d + \left(a + b\right)}{\frac{d - \left(b - a\right)}{d - \left(b - a\right)}}}\right) \cdot 2 \]

   2. *-inverses 95.0%

      \[\leadsto \left(c + \frac{d + \left(a + b\right)}{\color{blue}{1}}\right) \cdot 2 \]

   3. flip3-+ 95.2%

      \[\leadsto \left(c + \frac{\color{blue}{\frac{{d}^{3} + {\left(a + b\right)}^{3}}{d \cdot d + \left(\left(a + b\right) \cdot \left(a + b\right) - d \cdot \left(a + b\right)\right)}}}{1}\right) \cdot 2 \]

   4. associate-/l/ 95.2%

      \[\leadsto \left(c + \color{blue}{\frac{{d}^{3} + {\left(a + b\right)}^{3}}{1 \cdot \left(d \cdot d + \left(\left(a + b\right) \cdot \left(a + b\right) - d \cdot \left(a + b\right)\right)\right)}}\right) \cdot 2 \]

   5. *-un-lft-identity 95.2%

      \[\leadsto \left(c + \frac{{d}^{3} + {\left(a + b\right)}^{3}}{\color{blue}{d \cdot d + \left(\left(a + b\right) \cdot \left(a + b\right) - d \cdot \left(a + b\right)\right)}}\right) \cdot 2 \]

   6. flip3-+ 95.0%

      \[\leadsto \left(c + \color{blue}{\left(d + \left(a + b\right)\right)}\right) \cdot 2 \]

   7. associate-+r+ 100.0%

      \[\leadsto \left(c + \color{blue}{\left(\left(d + a\right) + b\right)}\right) \cdot 2 \]

   8. +-commutative 100.0%

      \[\leadsto \left(c + \left(\color{blue}{\left(a + d\right)} + b\right)\right) \cdot 2 \]

8. Applied egg-rr 100.0%

   \[\leadsto \left(c + \color{blue}{\left(\left(a + d\right) + b\right)}\right) \cdot 2 \]

9. Final simplification 100.0%

   \[\leadsto \left(c + \left(\left(a + d\right) + b\right)\right) \cdot 2 \]

Alternative 2: 94.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(a + \left(b + \left(c + d\right)\right)\right) \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* 2.0 (+ a (+ b (+ c d)))))

C

double code(double a, double b, double c, double d) {
	return 2.0 * (a + (b + (c + d)));
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = 2.0d0 * (a + (b + (c + d)))
end function

Java

public static double code(double a, double b, double c, double d) {
	return 2.0 * (a + (b + (c + d)));
}

Python

def code(a, b, c, d):
	return 2.0 * (a + (b + (c + d)))

Julia

function code(a, b, c, d)
	return Float64(2.0 * Float64(a + Float64(b + Float64(c + d))))
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = 2.0 * (a + (b + (c + d)));
end

Wolfram

code[a_, b_, c_, d_] := N[(2.0 * N[(a + N[(b + N[(c + d), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

2. Final simplification 94.2%

   \[\leadsto 2 \cdot \left(a + \left(b + \left(c + d\right)\right)\right) \]

Alternative 3: 95.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(c + \left(a + \left(d + b\right)\right)\right) \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* 2.0 (+ c (+ a (+ d b)))))

C

double code(double a, double b, double c, double d) {
	return 2.0 * (c + (a + (d + b)));
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = 2.0d0 * (c + (a + (d + b)))
end function

Java

public static double code(double a, double b, double c, double d) {
	return 2.0 * (c + (a + (d + b)));
}

Python

def code(a, b, c, d):
	return 2.0 * (c + (a + (d + b)))

Julia

function code(a, b, c, d)
	return Float64(2.0 * Float64(c + Float64(a + Float64(d + b))))
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = 2.0 * (c + (a + (d + b)));
end

Wolfram

code[a_, b_, c_, d_] := N[(2.0 * N[(c + N[(a + N[(d + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

2. Taylor expanded in a around 0 95.6%

   \[\leadsto \color{blue}{\left(c + \left(a + \left(d + b\right)\right)\right)} \cdot 2 \]

3. Final simplification 95.6%

   \[\leadsto 2 \cdot \left(c + \left(a + \left(d + b\right)\right)\right) \]

Alternative 4: 14.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -13.67:\\ \;\;\;\;\left(a + d\right) \cdot 2\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(c + b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (a b c d)
 :precision binary64
 (if (<= a -13.67) (* (+ a d) 2.0) (* 2.0 (+ c b))))

C

double code(double a, double b, double c, double d) {
	double tmp;
	if (a <= -13.67) {
		tmp = (a + d) * 2.0;
	} else {
		tmp = 2.0 * (c + b);
	}
	return tmp;
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    real(8) :: tmp
    if (a <= (-13.67d0)) then
        tmp = (a + d) * 2.0d0
    else
        tmp = 2.0d0 * (c + b)
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double c, double d) {
	double tmp;
	if (a <= -13.67) {
		tmp = (a + d) * 2.0;
	} else {
		tmp = 2.0 * (c + b);
	}
	return tmp;
}

Python

def code(a, b, c, d):
	tmp = 0
	if a <= -13.67:
		tmp = (a + d) * 2.0
	else:
		tmp = 2.0 * (c + b)
	return tmp

Julia

function code(a, b, c, d)
	tmp = 0.0
	if (a <= -13.67)
		tmp = Float64(Float64(a + d) * 2.0);
	else
		tmp = Float64(2.0 * Float64(c + b));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, c, d)
	tmp = 0.0;
	if (a <= -13.67)
		tmp = (a + d) * 2.0;
	else
		tmp = 2.0 * (c + b);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, c_, d_] := If[LessEqual[a, -13.67], N[(N[(a + d), $MachinePrecision] * 2.0), $MachinePrecision], N[(2.0 * N[(c + b), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -13.6699999999999999

   1. Initial program 93.4%

      \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

   2. Taylor expanded in b around 0 6.3%

      \[\leadsto \left(a + \color{blue}{\left(c + d\right)}\right) \cdot 2 \]

   3. Taylor expanded in c around 0 12.6%

      \[\leadsto \color{blue}{\left(a + d\right)} \cdot 2 \]
   4. Step-by-step derivation

      1. +-commutative 12.6%

         \[\leadsto \color{blue}{\left(d + a\right)} \cdot 2 \]

   5. Simplified 12.6%

      \[\leadsto \color{blue}{\left(d + a\right)} \cdot 2 \]

   if -13.6699999999999999 < a

   1. Initial program 94.6%

      \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

   2. Taylor expanded in a around 0 95.6%

      \[\leadsto \color{blue}{\left(c + \left(a + \left(d + b\right)\right)\right)} \cdot 2 \]

   3. Taylor expanded in b around inf 15.8%

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

4. Final simplification 14.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -13.67:\\ \;\;\;\;\left(a + d\right) \cdot 2\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(c + b\right)\\ \end{array} \]

Alternative 5: 12.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -13.66:\\ \;\;\;\;b \cdot 2\\ \mathbf{else}:\\ \;\;\;\;c \cdot 2\\ \end{array} \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (if (<= a -13.66) (* b 2.0) (* c 2.0)))

C

double code(double a, double b, double c, double d) {
	double tmp;
	if (a <= -13.66) {
		tmp = b * 2.0;
	} else {
		tmp = c * 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    real(8) :: tmp
    if (a <= (-13.66d0)) then
        tmp = b * 2.0d0
    else
        tmp = c * 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double c, double d) {
	double tmp;
	if (a <= -13.66) {
		tmp = b * 2.0;
	} else {
		tmp = c * 2.0;
	}
	return tmp;
}

Python

def code(a, b, c, d):
	tmp = 0
	if a <= -13.66:
		tmp = b * 2.0
	else:
		tmp = c * 2.0
	return tmp

Julia

function code(a, b, c, d)
	tmp = 0.0
	if (a <= -13.66)
		tmp = Float64(b * 2.0);
	else
		tmp = Float64(c * 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, c, d)
	tmp = 0.0;
	if (a <= -13.66)
		tmp = b * 2.0;
	else
		tmp = c * 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, c_, d_] := If[LessEqual[a, -13.66], N[(b * 2.0), $MachinePrecision], N[(c * 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -13.66

   1. Initial program 93.5%

      \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

   2. Taylor expanded in b around inf 11.4%

      \[\leadsto \color{blue}{b} \cdot 2 \]

   if -13.66 < a

   1. Initial program 94.6%

      \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

   2. Taylor expanded in c around inf 13.2%

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

4. Final simplification 12.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \leq -13.66:\\ \;\;\;\;b \cdot 2\\ \mathbf{else}:\\ \;\;\;\;c \cdot 2\\ \end{array} \]

Alternative 6: 14.0% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(c + b\right) \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* 2.0 (+ c b)))

C

double code(double a, double b, double c, double d) {
	return 2.0 * (c + b);
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = 2.0d0 * (c + b)
end function

Java

public static double code(double a, double b, double c, double d) {
	return 2.0 * (c + b);
}

Python

def code(a, b, c, d):
	return 2.0 * (c + b)

Julia

function code(a, b, c, d)
	return Float64(2.0 * Float64(c + b))
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = 2.0 * (c + b);
end

Wolfram

code[a_, b_, c_, d_] := N[(2.0 * N[(c + b), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

2. Taylor expanded in a around 0 95.6%

   \[\leadsto \color{blue}{\left(c + \left(a + \left(d + b\right)\right)\right)} \cdot 2 \]

3. Taylor expanded in b around inf 12.9%

   \[\leadsto \left(c + \color{blue}{b}\right) \cdot 2 \]

4. Final simplification 12.9%

   \[\leadsto 2 \cdot \left(c + b\right) \]

Alternative 7: 6.2% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ b \cdot 2 \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (* b 2.0))

C

double code(double a, double b, double c, double d) {
	return b * 2.0;
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = b * 2.0d0
end function

Java

public static double code(double a, double b, double c, double d) {
	return b * 2.0;
}

Python

def code(a, b, c, d):
	return b * 2.0

Julia

function code(a, b, c, d)
	return Float64(b * 2.0)
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = b * 2.0;
end

Wolfram

code[a_, b_, c_, d_] := N[(b * 2.0), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\left(a + \left(b + \left(c + d\right)\right)\right) \cdot 2 \]

2. Taylor expanded in b around inf 6.9%

   \[\leadsto \color{blue}{b} \cdot 2 \]

3. Final simplification 6.9%

   \[\leadsto b \cdot 2 \]

Developer target: 94.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \left(a + b\right) \cdot 2 + \left(c + d\right) \cdot 2 \end{array} \]

FPCore

(FPCore (a b c d) :precision binary64 (+ (* (+ a b) 2.0) (* (+ c d) 2.0)))

C

double code(double a, double b, double c, double d) {
	return ((a + b) * 2.0) + ((c + d) * 2.0);
}

Fortran

real(8) function code(a, b, c, d)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8), intent (in) :: d
    code = ((a + b) * 2.0d0) + ((c + d) * 2.0d0)
end function

Java

public static double code(double a, double b, double c, double d) {
	return ((a + b) * 2.0) + ((c + d) * 2.0);
}

Python

def code(a, b, c, d):
	return ((a + b) * 2.0) + ((c + d) * 2.0)

Julia

function code(a, b, c, d)
	return Float64(Float64(Float64(a + b) * 2.0) + Float64(Float64(c + d) * 2.0))
end

MATLAB

function tmp = code(a, b, c, d)
	tmp = ((a + b) * 2.0) + ((c + d) * 2.0);
end

Wolfram

code[a_, b_, c_, d_] := N[(N[(N[(a + b), $MachinePrecision] * 2.0), $MachinePrecision] + N[(N[(c + d), $MachinePrecision] * 2.0), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023178 
(FPCore (a b c d)
  :name "Expression, p6"
  :precision binary64
  :pre (and (and (and (and (<= -14.0 a) (<= a -13.0)) (and (<= -3.0 b) (<= b -2.0))) (and (<= 3.0 c) (<= c 3.5))) (and (<= 12.5 d) (<= d 13.5)))

  :herbie-target
  (+ (* (+ a b) 2.0) (* (+ c d) 2.0))

  (* (+ a (+ b (+ c d))) 2.0))