Diagrams.Solve.Polynomial:quartForm from diagrams-solve-0.1, A

Percentage Accurate: 100.0% → 100.0%

Time: 2.6s

Alternatives: 4

Speedup: 1.4×

• could not determine a ground truth

  1. x = 4.118740720107076e+39
  2. y = -1.2781856575180408e+163

Specification

Math

\[\begin{array}{l} \\ x - \frac{3}{8} \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- x (* (/ 3.0 8.0) y)))

C

double code(double x, double y) {
	return x - ((3.0 / 8.0) * y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x - ((3.0d0 / 8.0d0) * y)
end function

Java

public static double code(double x, double y) {
	return x - ((3.0 / 8.0) * y);
}

Python

def code(x, y):
	return x - ((3.0 / 8.0) * y)

Julia

function code(x, y)
	return Float64(x - Float64(Float64(3.0 / 8.0) * y))
end

MATLAB

function tmp = code(x, y)
	tmp = x - ((3.0 / 8.0) * y);
end

Wolfram

code[x_, y_] := N[(x - N[(N[(3.0 / 8.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \frac{3}{8} \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- x (* (/ 3.0 8.0) y)))

C

double code(double x, double y) {
	return x - ((3.0 / 8.0) * y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x - ((3.0d0 / 8.0d0) * y)
end function

Java

public static double code(double x, double y) {
	return x - ((3.0 / 8.0) * y);
}

Python

def code(x, y):
	return x - ((3.0 / 8.0) * y)

Julia

function code(x, y)
	return Float64(x - Float64(Float64(3.0 / 8.0) * y))
end

MATLAB

function tmp = code(x, y)
	tmp = x - ((3.0 / 8.0) * y);
end

Wolfram

code[x_, y_] := N[(x - N[(N[(3.0 / 8.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, -0.375, x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma y -0.375 x))

C

double code(double x, double y) {
	return fma(y, -0.375, x);
}

Julia

function code(x, y)
	return fma(y, -0.375, x)
end

Wolfram

code[x_, y_] := N[(y * -0.375 + x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{3}{8} \cdot y \]
2. Step-by-step derivation

   1. sub-neg 100.0%

      \[\leadsto \color{blue}{x + \left(-\frac{3}{8} \cdot y\right)} \]

   2. distribute-rgt-neg-out 100.0%

      \[\leadsto x + \color{blue}{\frac{3}{8} \cdot \left(-y\right)} \]

   3. +-commutative 100.0%

      \[\leadsto \color{blue}{\frac{3}{8} \cdot \left(-y\right) + x} \]

   4. distribute-rgt-neg-out 100.0%

      \[\leadsto \color{blue}{\left(-\frac{3}{8} \cdot y\right)} + x \]

   5. *-commutative 100.0%

      \[\leadsto \left(-\color{blue}{y \cdot \frac{3}{8}}\right) + x \]

   6. distribute-rgt-neg-in 100.0%

      \[\leadsto \color{blue}{y \cdot \left(-\frac{3}{8}\right)} + x \]

   7. fma-define 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, -\frac{3}{8}, x\right)} \]

   8. metadata-eval 100.0%

      \[\leadsto \mathsf{fma}\left(y, -\color{blue}{0.375}, x\right) \]

   9. metadata-eval 100.0%

      \[\leadsto \mathsf{fma}\left(y, \color{blue}{-0.375}, x\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, -0.375, x\right)} \]
4. Add Preprocessing

5. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(y, -0.375, x\right) \]
6. Add Preprocessing

Alternative 2: 74.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+196} \lor \neg \left(y \leq -2.6 \cdot 10^{+166}\right) \land \left(y \leq -7.2 \cdot 10^{+20} \lor \neg \left(y \leq 1.5\right)\right):\\ \;\;\;\;y \cdot -0.375\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -6e+196)
         (and (not (<= y -2.6e+166)) (or (<= y -7.2e+20) (not (<= y 1.5)))))
   (* y -0.375)
   x))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -6e+196) || (!(y <= -2.6e+166) && ((y <= -7.2e+20) || !(y <= 1.5)))) {
		tmp = y * -0.375;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-6d+196)) .or. (.not. (y <= (-2.6d+166))) .and. (y <= (-7.2d+20)) .or. (.not. (y <= 1.5d0))) then
        tmp = y * (-0.375d0)
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -6e+196) || (!(y <= -2.6e+166) && ((y <= -7.2e+20) || !(y <= 1.5)))) {
		tmp = y * -0.375;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -6e+196) or (not (y <= -2.6e+166) and ((y <= -7.2e+20) or not (y <= 1.5))):
		tmp = y * -0.375
	else:
		tmp = x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -6e+196) || (!(y <= -2.6e+166) && ((y <= -7.2e+20) || !(y <= 1.5))))
		tmp = Float64(y * -0.375);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -6e+196) || (~((y <= -2.6e+166)) && ((y <= -7.2e+20) || ~((y <= 1.5)))))
		tmp = y * -0.375;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -6e+196], And[N[Not[LessEqual[y, -2.6e+166]], $MachinePrecision], Or[LessEqual[y, -7.2e+20], N[Not[LessEqual[y, 1.5]], $MachinePrecision]]]], N[(y * -0.375), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if y < -5.9999999999999997e196 or -2.5999999999999999e166 < y < -7.2e20 or 1.5 < y

   1. Initial program 100.0%

      \[x - \frac{3}{8} \cdot y \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

         \[\leadsto \color{blue}{x + \left(-\frac{3}{8} \cdot y\right)} \]

      2. *-commutative 100.0%

         \[\leadsto x + \left(-\color{blue}{y \cdot \frac{3}{8}}\right) \]

      3. distribute-rgt-neg-in 100.0%

         \[\leadsto x + \color{blue}{y \cdot \left(-\frac{3}{8}\right)} \]

      4. metadata-eval 100.0%

         \[\leadsto x + y \cdot \left(-\color{blue}{0.375}\right) \]

      5. metadata-eval 100.0%

         \[\leadsto x + y \cdot \color{blue}{-0.375} \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x + y \cdot -0.375} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 76.2%

      \[\leadsto \color{blue}{-0.375 \cdot y} \]

   if -5.9999999999999997e196 < y < -2.5999999999999999e166 or -7.2e20 < y < 1.5

   1. Initial program 100.0%

      \[x - \frac{3}{8} \cdot y \]
   2. Step-by-step derivation

      1. sub-neg 100.0%

         \[\leadsto \color{blue}{x + \left(-\frac{3}{8} \cdot y\right)} \]

      2. *-commutative 100.0%

         \[\leadsto x + \left(-\color{blue}{y \cdot \frac{3}{8}}\right) \]

      3. distribute-rgt-neg-in 100.0%

         \[\leadsto x + \color{blue}{y \cdot \left(-\frac{3}{8}\right)} \]

      4. metadata-eval 100.0%

         \[\leadsto x + y \cdot \left(-\color{blue}{0.375}\right) \]

      5. metadata-eval 100.0%

         \[\leadsto x + y \cdot \color{blue}{-0.375} \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{x + y \cdot -0.375} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 82.4%

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

4. Final simplification 79.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+196} \lor \neg \left(y \leq -2.6 \cdot 10^{+166}\right) \land \left(y \leq -7.2 \cdot 10^{+20} \lor \neg \left(y \leq 1.5\right)\right):\\ \;\;\;\;y \cdot -0.375\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ x + y \cdot -0.375 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (* y -0.375)))

C

double code(double x, double y) {
	return x + (y * -0.375);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (y * (-0.375d0))
end function

Java

public static double code(double x, double y) {
	return x + (y * -0.375);
}

Python

def code(x, y):
	return x + (y * -0.375)

Julia

function code(x, y)
	return Float64(x + Float64(y * -0.375))
end

MATLAB

function tmp = code(x, y)
	tmp = x + (y * -0.375);
end

Wolfram

code[x_, y_] := N[(x + N[(y * -0.375), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{3}{8} \cdot y \]
2. Step-by-step derivation

   1. sub-neg 100.0%

      \[\leadsto \color{blue}{x + \left(-\frac{3}{8} \cdot y\right)} \]

   2. *-commutative 100.0%

      \[\leadsto x + \left(-\color{blue}{y \cdot \frac{3}{8}}\right) \]

   3. distribute-rgt-neg-in 100.0%

      \[\leadsto x + \color{blue}{y \cdot \left(-\frac{3}{8}\right)} \]

   4. metadata-eval 100.0%

      \[\leadsto x + y \cdot \left(-\color{blue}{0.375}\right) \]

   5. metadata-eval 100.0%

      \[\leadsto x + y \cdot \color{blue}{-0.375} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{x + y \cdot -0.375} \]
4. Add Preprocessing

5. Final simplification 100.0%

   \[\leadsto x + y \cdot -0.375 \]
6. Add Preprocessing

Alternative 4: 54.7% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 x)

C

double code(double x, double y) {
	return x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x
end function

Java

public static double code(double x, double y) {
	return x;
}

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

function tmp = code(x, y)
	tmp = x;
end

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 100.0%

   \[x - \frac{3}{8} \cdot y \]
2. Step-by-step derivation

   1. sub-neg 100.0%

      \[\leadsto \color{blue}{x + \left(-\frac{3}{8} \cdot y\right)} \]

   2. *-commutative 100.0%

      \[\leadsto x + \left(-\color{blue}{y \cdot \frac{3}{8}}\right) \]

   3. distribute-rgt-neg-in 100.0%

      \[\leadsto x + \color{blue}{y \cdot \left(-\frac{3}{8}\right)} \]

   4. metadata-eval 100.0%

      \[\leadsto x + y \cdot \left(-\color{blue}{0.375}\right) \]

   5. metadata-eval 100.0%

      \[\leadsto x + y \cdot \color{blue}{-0.375} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{x + y \cdot -0.375} \]
4. Add Preprocessing

5. Taylor expanded in x around inf 57.0%

   \[\leadsto \color{blue}{x} \]

6. Final simplification 57.0%

   \[\leadsto x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024052 
(FPCore (x y)
  :name "Diagrams.Solve.Polynomial:quartForm  from diagrams-solve-0.1, A"
  :precision binary64
  (- x (* (/ 3.0 8.0) y)))