Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, H

Average Accuracy: 93.5% → 97.6%

Time: 14.2s

Precision: binary64

Cost: 969

Math

\[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]

↓

\[\begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{-76} \lor \neg \left(y \leq 8.6 \cdot 10^{-129}\right):\\ \;\;\;\;x + \frac{y - \frac{t}{y}}{z \cdot -3}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{0.3333333333333333}{y \cdot \frac{z}{t}}\\ \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- x (/ y (* z 3.0))) (/ t (* (* z 3.0) y))))

↓

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -6.6e-76) (not (<= y 8.6e-129)))
   (+ x (/ (- y (/ t y)) (* z -3.0)))
   (+ x (/ 0.3333333333333333 (* y (/ z t))))))

C

double code(double x, double y, double z, double t) {
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
}

↓

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -6.6e-76) || !(y <= 8.6e-129)) {
		tmp = x + ((y - (t / y)) / (z * -3.0));
	} else {
		tmp = x + (0.3333333333333333 / (y * (z / t)));
	}
	return tmp;
}

Fortran

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

↓

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-6.6d-76)) .or. (.not. (y <= 8.6d-129))) then
        tmp = x + ((y - (t / y)) / (z * (-3.0d0)))
    else
        tmp = x + (0.3333333333333333d0 / (y * (z / t)))
    end if
    code = tmp
end function

Java

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

↓

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -6.6e-76) || !(y <= 8.6e-129)) {
		tmp = x + ((y - (t / y)) / (z * -3.0));
	} else {
		tmp = x + (0.3333333333333333 / (y * (z / t)));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	return (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y))

↓

def code(x, y, z, t):
	tmp = 0
	if (y <= -6.6e-76) or not (y <= 8.6e-129):
		tmp = x + ((y - (t / y)) / (z * -3.0))
	else:
		tmp = x + (0.3333333333333333 / (y * (z / t)))
	return tmp

Julia

function code(x, y, z, t)
	return Float64(Float64(x - Float64(y / Float64(z * 3.0))) + Float64(t / Float64(Float64(z * 3.0) * y)))
end

↓

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -6.6e-76) || !(y <= 8.6e-129))
		tmp = Float64(x + Float64(Float64(y - Float64(t / y)) / Float64(z * -3.0)));
	else
		tmp = Float64(x + Float64(0.3333333333333333 / Float64(y * Float64(z / t))));
	end
	return tmp
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x - (y / (z * 3.0))) + (t / ((z * 3.0) * y));
end

↓

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -6.6e-76) || ~((y <= 8.6e-129)))
		tmp = x + ((y - (t / y)) / (z * -3.0));
	else
		tmp = x + (0.3333333333333333 / (y * (z / t)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x - N[(y / N[(z * 3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(t / N[(N[(z * 3.0), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -6.6e-76], N[Not[LessEqual[y, 8.6e-129]], $MachinePrecision]], N[(x + N[(N[(y - N[(t / y), $MachinePrecision]), $MachinePrecision] / N[(z * -3.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(0.3333333333333333 / N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Target

Original	93.5%
Target	97.4%
Herbie	97.6%

\[\left(x - \frac{y}{z \cdot 3}\right) + \frac{\frac{t}{z \cdot 3}}{y} \]

Derivation

1. Split input into 2 regimes

2. if y < -6.59999999999999967e-76 or 8.59999999999999962e-129 < y

   1. Initial program 98.0%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]

   2. Simplified 98.5%

      \[\leadsto \color{blue}{x + \frac{-0.3333333333333333}{z} \cdot \left(y - \frac{t}{y}\right)} \]
      Proof

      [Start] 98.0	\[ \left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
      associate-+l- [=>] 98.0	\[ \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]
      sub-neg [=>] 98.0	\[ \color{blue}{x + \left(-\left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)\right)} \]
      neg-mul-1 [=>] 98.0	\[ x + \color{blue}{-1 \cdot \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]
      distribute-lft-out-- [<=] 98.0	\[ x + \color{blue}{\left(-1 \cdot \frac{y}{z \cdot 3} - -1 \cdot \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]
      associate-*r/ [=>] 98.0	\[ x + \left(\color{blue}{\frac{-1 \cdot y}{z \cdot 3}} - -1 \cdot \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]
      associate-*l/ [<=] 97.9	\[ x + \left(\color{blue}{\frac{-1}{z \cdot 3} \cdot y} - -1 \cdot \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]
      associate-*r/ [=>] 97.9	\[ x + \left(\frac{-1}{z \cdot 3} \cdot y - \color{blue}{\frac{-1 \cdot t}{\left(z \cdot 3\right) \cdot y}}\right) \]
      times-frac [=>] 98.4	\[ x + \left(\frac{-1}{z \cdot 3} \cdot y - \color{blue}{\frac{-1}{z \cdot 3} \cdot \frac{t}{y}}\right) \]
      distribute-lft-out-- [=>] 98.4	\[ x + \color{blue}{\frac{-1}{z \cdot 3} \cdot \left(y - \frac{t}{y}\right)} \]
      *-commutative [=>] 98.4	\[ x + \frac{-1}{\color{blue}{3 \cdot z}} \cdot \left(y - \frac{t}{y}\right) \]
      associate-/r* [=>] 98.5	\[ x + \color{blue}{\frac{\frac{-1}{3}}{z}} \cdot \left(y - \frac{t}{y}\right) \]
      metadata-eval [=>] 98.5	\[ x + \frac{\color{blue}{-0.3333333333333333}}{z} \cdot \left(y - \frac{t}{y}\right) \]

   3. Applied egg-rr 98.5%

      \[\leadsto x + \color{blue}{\frac{y - \frac{t}{y}}{z \cdot -3}} \]
      Proof

      [Start] 98.5	\[ x + \frac{-0.3333333333333333}{z} \cdot \left(y - \frac{t}{y}\right) \]
      clear-num [=>] 98.3	\[ x + \color{blue}{\frac{1}{\frac{z}{-0.3333333333333333}}} \cdot \left(y - \frac{t}{y}\right) \]
      associate-*l/ [=>] 98.4	\[ x + \color{blue}{\frac{1 \cdot \left(y - \frac{t}{y}\right)}{\frac{z}{-0.3333333333333333}}} \]
      *-un-lft-identity [<=] 98.4	\[ x + \frac{\color{blue}{y - \frac{t}{y}}}{\frac{z}{-0.3333333333333333}} \]
      div-inv [=>] 98.5	\[ x + \frac{y - \frac{t}{y}}{\color{blue}{z \cdot \frac{1}{-0.3333333333333333}}} \]
      metadata-eval [=>] 98.5	\[ x + \frac{y - \frac{t}{y}}{z \cdot \color{blue}{-3}} \]

   if -6.59999999999999967e-76 < y < 8.59999999999999962e-129

   1. Initial program 83.4%

      \[\left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]

   2. Simplified 82.8%

      \[\leadsto \color{blue}{x + \frac{-0.3333333333333333}{z} \cdot \left(y - \frac{t}{y}\right)} \]
      Proof

      [Start] 83.4	\[ \left(x - \frac{y}{z \cdot 3}\right) + \frac{t}{\left(z \cdot 3\right) \cdot y} \]
      associate-+l- [=>] 83.4	\[ \color{blue}{x - \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]
      sub-neg [=>] 83.4	\[ \color{blue}{x + \left(-\left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)\right)} \]
      neg-mul-1 [=>] 83.4	\[ x + \color{blue}{-1 \cdot \left(\frac{y}{z \cdot 3} - \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]
      distribute-lft-out-- [<=] 83.4	\[ x + \color{blue}{\left(-1 \cdot \frac{y}{z \cdot 3} - -1 \cdot \frac{t}{\left(z \cdot 3\right) \cdot y}\right)} \]
      associate-*r/ [=>] 83.4	\[ x + \left(\color{blue}{\frac{-1 \cdot y}{z \cdot 3}} - -1 \cdot \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]
      associate-*l/ [<=] 83.4	\[ x + \left(\color{blue}{\frac{-1}{z \cdot 3} \cdot y} - -1 \cdot \frac{t}{\left(z \cdot 3\right) \cdot y}\right) \]
      associate-*r/ [=>] 83.4	\[ x + \left(\frac{-1}{z \cdot 3} \cdot y - \color{blue}{\frac{-1 \cdot t}{\left(z \cdot 3\right) \cdot y}}\right) \]
      times-frac [=>] 82.8	\[ x + \left(\frac{-1}{z \cdot 3} \cdot y - \color{blue}{\frac{-1}{z \cdot 3} \cdot \frac{t}{y}}\right) \]
      distribute-lft-out-- [=>] 82.8	\[ x + \color{blue}{\frac{-1}{z \cdot 3} \cdot \left(y - \frac{t}{y}\right)} \]
      *-commutative [=>] 82.8	\[ x + \frac{-1}{\color{blue}{3 \cdot z}} \cdot \left(y - \frac{t}{y}\right) \]
      associate-/r* [=>] 82.8	\[ x + \color{blue}{\frac{\frac{-1}{3}}{z}} \cdot \left(y - \frac{t}{y}\right) \]
      metadata-eval [=>] 82.8	\[ x + \frac{\color{blue}{-0.3333333333333333}}{z} \cdot \left(y - \frac{t}{y}\right) \]

   3. Taylor expanded in y around 0 81.5%

      \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot \color{blue}{\left(-1 \cdot \frac{t}{y}\right)} \]

   4. Simplified 81.5%

      \[\leadsto x + \frac{-0.3333333333333333}{z} \cdot \color{blue}{\left(-\frac{t}{y}\right)} \]
      Proof

      [Start] 81.5	\[ x + \frac{-0.3333333333333333}{z} \cdot \left(-1 \cdot \frac{t}{y}\right) \]
      mul-1-neg [=>] 81.5	\[ x + \frac{-0.3333333333333333}{z} \cdot \color{blue}{\left(-\frac{t}{y}\right)} \]

   5. Applied egg-rr 95.5%

      \[\leadsto \color{blue}{x - \frac{-0.3333333333333333}{\frac{z}{t} \cdot y}} \]
      Proof

      [Start] 81.5	\[ x + \frac{-0.3333333333333333}{z} \cdot \left(-\frac{t}{y}\right) \]
      distribute-rgt-neg-out [=>] 81.5	\[ x + \color{blue}{\left(-\frac{-0.3333333333333333}{z} \cdot \frac{t}{y}\right)} \]
      distribute-lft-neg-in [=>] 81.5	\[ x + \color{blue}{\left(-\frac{-0.3333333333333333}{z}\right) \cdot \frac{t}{y}} \]
      add-sqr-sqrt [=>] 40.7	\[ x + \left(-\frac{-0.3333333333333333}{z}\right) \cdot \color{blue}{\left(\sqrt{\frac{t}{y}} \cdot \sqrt{\frac{t}{y}}\right)} \]
      sqrt-unprod [=>] 44.0	\[ x + \left(-\frac{-0.3333333333333333}{z}\right) \cdot \color{blue}{\sqrt{\frac{t}{y} \cdot \frac{t}{y}}} \]
      sqr-neg [<=] 44.0	\[ x + \left(-\frac{-0.3333333333333333}{z}\right) \cdot \sqrt{\color{blue}{\left(-\frac{t}{y}\right) \cdot \left(-\frac{t}{y}\right)}} \]
      sqrt-unprod [<=] 18.5	\[ x + \left(-\frac{-0.3333333333333333}{z}\right) \cdot \color{blue}{\left(\sqrt{-\frac{t}{y}} \cdot \sqrt{-\frac{t}{y}}\right)} \]
      add-sqr-sqrt [<=] 37.1	\[ x + \left(-\frac{-0.3333333333333333}{z}\right) \cdot \color{blue}{\left(-\frac{t}{y}\right)} \]
      cancel-sign-sub-inv [<=] 37.1	\[ \color{blue}{x - \frac{-0.3333333333333333}{z} \cdot \left(-\frac{t}{y}\right)} \]
      associate-/r/ [<=] 37.1	\[ x - \color{blue}{\frac{-0.3333333333333333}{\frac{z}{-\frac{t}{y}}}} \]
      add-sqr-sqrt [=>] 18.5	\[ x - \frac{-0.3333333333333333}{\frac{z}{\color{blue}{\sqrt{-\frac{t}{y}} \cdot \sqrt{-\frac{t}{y}}}}} \]
      sqrt-unprod [=>] 44.0	\[ x - \frac{-0.3333333333333333}{\frac{z}{\color{blue}{\sqrt{\left(-\frac{t}{y}\right) \cdot \left(-\frac{t}{y}\right)}}}} \]
      sqr-neg [=>] 44.0	\[ x - \frac{-0.3333333333333333}{\frac{z}{\sqrt{\color{blue}{\frac{t}{y} \cdot \frac{t}{y}}}}} \]
      sqrt-unprod [<=] 40.7	\[ x - \frac{-0.3333333333333333}{\frac{z}{\color{blue}{\sqrt{\frac{t}{y}} \cdot \sqrt{\frac{t}{y}}}}} \]
      add-sqr-sqrt [<=] 81.5	\[ x - \frac{-0.3333333333333333}{\frac{z}{\color{blue}{\frac{t}{y}}}} \]
      associate-/r/ [=>] 95.5	\[ x - \frac{-0.3333333333333333}{\color{blue}{\frac{z}{t} \cdot y}} \]

3. Recombined 2 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -6.6 \cdot 10^{-76} \lor \neg \left(y \leq 8.6 \cdot 10^{-129}\right):\\ \;\;\;\;x + \frac{y - \frac{t}{y}}{z \cdot -3}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{0.3333333333333333}{y \cdot \frac{z}{t}}\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	48.6%
Cost	1112

\[\begin{array}{l} \mathbf{if}\;y \leq -2.8 \cdot 10^{+93}:\\ \;\;\;\;y \cdot \frac{-0.3333333333333333}{z}\\ \mathbf{elif}\;y \leq 6.2 \cdot 10^{-238}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{-116}:\\ \;\;\;\;0.3333333333333333 \cdot \frac{t}{y \cdot z}\\ \mathbf{elif}\;y \leq 2.85 \cdot 10^{+42}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{+82}:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq 4.2 \cdot 10^{+179}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z \cdot -3}\\ \end{array} \]

Alternative 2
Accuracy	49.4%
Cost	1112

\[\begin{array}{l} \mathbf{if}\;y \leq -2.65 \cdot 10^{+95}:\\ \;\;\;\;y \cdot \frac{-0.3333333333333333}{z}\\ \mathbf{elif}\;y \leq 5.8 \cdot 10^{-242}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{-117}:\\ \;\;\;\;0.3333333333333333 \cdot \frac{\frac{t}{z}}{y}\\ \mathbf{elif}\;y \leq 2.85 \cdot 10^{+42}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 3.2 \cdot 10^{+81}:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq 2.8 \cdot 10^{+180}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z \cdot -3}\\ \end{array} \]

Alternative 3
Accuracy	49.3%
Cost	1112

\[\begin{array}{l} \mathbf{if}\;y \leq -1.8 \cdot 10^{+96}:\\ \;\;\;\;\frac{1}{z} \cdot \frac{y}{-3}\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{-241}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 2.2 \cdot 10^{-117}:\\ \;\;\;\;0.3333333333333333 \cdot \frac{\frac{t}{z}}{y}\\ \mathbf{elif}\;y \leq 2.65 \cdot 10^{+42}:\\ \;\;\;\;x\\ \mathbf{elif}\;y \leq 1.6 \cdot 10^{+81}:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq 3.9 \cdot 10^{+180}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{z \cdot -3}\\ \end{array} \]

Alternative 4
Accuracy	97.6%
Cost	969

\[\begin{array}{l} \mathbf{if}\;y \leq -3.8 \cdot 10^{-77} \lor \neg \left(y \leq 2.7 \cdot 10^{-128}\right):\\ \;\;\;\;x + \left(y - \frac{t}{y}\right) \cdot \frac{-0.3333333333333333}{z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{0.3333333333333333}{y \cdot \frac{z}{t}}\\ \end{array} \]

Alternative 5
Accuracy	97.4%
Cost	960

\[\left(x - \frac{y}{z \cdot 3}\right) + \frac{\frac{t}{z \cdot 3}}{y} \]

Alternative 6
Accuracy	97.6%
Cost	960

\[\left(x - \frac{y}{z \cdot 3}\right) + \frac{0.3333333333333333}{y \cdot \frac{z}{t}} \]

Alternative 7
Accuracy	80.9%
Cost	841

\[\begin{array}{l} \mathbf{if}\;x \leq -9.5 \cdot 10^{-109} \lor \neg \left(x \leq 1.6 \cdot 10^{-76}\right):\\ \;\;\;\;x + -0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;-0.3333333333333333 \cdot \frac{y - \frac{t}{y}}{z}\\ \end{array} \]

Alternative 8
Accuracy	86.0%
Cost	841

\[\begin{array}{l} \mathbf{if}\;y \leq -9.1 \cdot 10^{+55} \lor \neg \left(y \leq 1.05 \cdot 10^{-12}\right):\\ \;\;\;\;x + -0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{t}{y \cdot \left(z \cdot 3\right)}\\ \end{array} \]

Alternative 9
Accuracy	91.2%
Cost	841

\[\begin{array}{l} \mathbf{if}\;y \leq -4.8 \cdot 10^{-11} \lor \neg \left(y \leq 6.8 \cdot 10^{-14}\right):\\ \;\;\;\;x + -0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;x + \frac{0.3333333333333333}{y \cdot \frac{z}{t}}\\ \end{array} \]

Alternative 10
Accuracy	72.1%
Cost	713

\[\begin{array}{l} \mathbf{if}\;y \leq 4.4 \cdot 10^{-243} \lor \neg \left(y \leq 8 \cdot 10^{-117}\right):\\ \;\;\;\;x + -0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;0.3333333333333333 \cdot \frac{\frac{t}{z}}{y}\\ \end{array} \]

Alternative 11
Accuracy	72.0%
Cost	713

\[\begin{array}{l} \mathbf{if}\;y \leq 8 \cdot 10^{-241} \lor \neg \left(y \leq 1.05 \cdot 10^{-117}\right):\\ \;\;\;\;x + -0.3333333333333333 \cdot \frac{y}{z}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{t}{z}}{y}}{3}\\ \end{array} \]

Alternative 12
Accuracy	55.5%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -1.4 \cdot 10^{-91}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 6.7 \cdot 10^{-69}:\\ \;\;\;\;y \cdot \frac{-0.3333333333333333}{z}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 13
Accuracy	55.5%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -1.4 \cdot 10^{-91}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 7.4 \cdot 10^{-69}:\\ \;\;\;\;\frac{y}{z \cdot -3}\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 14
Accuracy	41.8%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023138 
(FPCore (x y z t)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, H"
  :precision binary64

  :herbie-target
  (+ (- x (/ y (* z 3.0))) (/ (/ t (* z 3.0)) y))

  (+ (- x (/ y (* z 3.0))) (/ t (* (* z 3.0) y))))