Linear.Quaternion:$ccosh from linear-1.19.1.3

Average Error: 14.1 → 0.1

Time: 6.8s

Precision: binary64

Cost: 13120

Math

\[\frac{\sin x \cdot \sinh y}{x} \]

↓

\[\frac{\sinh y}{\frac{x}{\sin x}} \]

FPCore

(FPCore (x y) :precision binary64 (/ (* (sin x) (sinh y)) x))

↓

(FPCore (x y) :precision binary64 (/ (sinh y) (/ x (sin x))))

C

double code(double x, double y) {
	return (sin(x) * sinh(y)) / x;
}

↓

double code(double x, double y) {
	return sinh(y) / (x / sin(x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (sin(x) * sinh(y)) / x
end function

↓

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

Java

public static double code(double x, double y) {
	return (Math.sin(x) * Math.sinh(y)) / x;
}

↓

public static double code(double x, double y) {
	return Math.sinh(y) / (x / Math.sin(x));
}

Python

def code(x, y):
	return (math.sin(x) * math.sinh(y)) / x

↓

def code(x, y):
	return math.sinh(y) / (x / math.sin(x))

Julia

function code(x, y)
	return Float64(Float64(sin(x) * sinh(y)) / x)
end

↓

function code(x, y)
	return Float64(sinh(y) / Float64(x / sin(x)))
end

MATLAB

function tmp = code(x, y)
	tmp = (sin(x) * sinh(y)) / x;
end

↓

function tmp = code(x, y)
	tmp = sinh(y) / (x / sin(x));
end

Wolfram

code[x_, y_] := N[(N[(N[Sin[x], $MachinePrecision] * N[Sinh[y], $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]

↓

code[x_, y_] := N[(N[Sinh[y], $MachinePrecision] / N[(x / N[Sin[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Target

Original	14.1
Target	0.2
Herbie	0.1

\[\sin x \cdot \frac{\sinh y}{x} \]

Derivation

1. Initial program 14.1

   \[\frac{\sin x \cdot \sinh y}{x} \]

2. Simplified 0.1

   \[\leadsto \color{blue}{\frac{\sin x}{x} \cdot \sinh y} \]
   Proof
   (*.f64 (/.f64 (sin.f64 x) x) (sinh.f64 y)): 0 points increase in error, 0 points decrease in error
   (Rewrite=> associate-*l/_binary64 (/.f64 (*.f64 (sin.f64 x) (sinh.f64 y)) x)): 69 points increase in error, 10 points decrease in error

3. Applied egg-rr 0.1

   \[\leadsto \color{blue}{\frac{\sinh y}{\frac{x}{\sin x}}} \]

4. Final simplification 0.1

   \[\leadsto \frac{\sinh y}{\frac{x}{\sin x}} \]

Alternatives

Alternative 1
Error	0.2
Cost	13120

\[\sin x \cdot \frac{\sinh y}{x} \]

Alternative 2
Error	0.1
Cost	13120

\[\sinh y \cdot \frac{\sin x}{x} \]

Alternative 3
Error	1.4
Cost	6720

\[\sin x \cdot \frac{y}{x} \]

Alternative 4
Error	1.3
Cost	6720

\[y \cdot \frac{\sin x}{x} \]

Alternative 5
Error	1.3
Cost	6720

\[\frac{y}{\frac{x}{\sin x}} \]

Alternative 6
Error	17.0
Cost	320

\[\frac{x}{\frac{x}{y}} \]

Alternative 7
Error	31.3
Cost	64

\[y \]

Reproduce

herbie shell --seed 2022331 
(FPCore (x y)
  :name "Linear.Quaternion:$ccosh from linear-1.19.1.3"
  :precision binary64

  :herbie-target
  (* (sin x) (/ (sinh y) x))

  (/ (* (sin x) (sinh y)) x))