?

Average Error: 62.0 → 48.6
Time: 10.0s
Precision: binary64
Cost: 1088

?

\[lo < -1 \cdot 10^{+308} \land hi > 10^{+308}\]
\[\frac{x - lo}{hi - lo} \]
\[\left(\left(x + x\right) - lo\right) \cdot \left(2 \cdot \frac{0.25}{hi}\right) + \frac{lo}{hi \cdot -2} \]
(FPCore (lo hi x) :precision binary64 (/ (- x lo) (- hi lo)))
(FPCore (lo hi x)
 :precision binary64
 (+ (* (- (+ x x) lo) (* 2.0 (/ 0.25 hi))) (/ lo (* hi -2.0))))
double code(double lo, double hi, double x) {
	return (x - lo) / (hi - lo);
}
double code(double lo, double hi, double x) {
	return (((x + x) - lo) * (2.0 * (0.25 / hi))) + (lo / (hi * -2.0));
}
real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = (x - lo) / (hi - lo)
end function
real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = (((x + x) - lo) * (2.0d0 * (0.25d0 / hi))) + (lo / (hi * (-2.0d0)))
end function
public static double code(double lo, double hi, double x) {
	return (x - lo) / (hi - lo);
}
public static double code(double lo, double hi, double x) {
	return (((x + x) - lo) * (2.0 * (0.25 / hi))) + (lo / (hi * -2.0));
}
def code(lo, hi, x):
	return (x - lo) / (hi - lo)
def code(lo, hi, x):
	return (((x + x) - lo) * (2.0 * (0.25 / hi))) + (lo / (hi * -2.0))
function code(lo, hi, x)
	return Float64(Float64(x - lo) / Float64(hi - lo))
end
function code(lo, hi, x)
	return Float64(Float64(Float64(Float64(x + x) - lo) * Float64(2.0 * Float64(0.25 / hi))) + Float64(lo / Float64(hi * -2.0)))
end
function tmp = code(lo, hi, x)
	tmp = (x - lo) / (hi - lo);
end
function tmp = code(lo, hi, x)
	tmp = (((x + x) - lo) * (2.0 * (0.25 / hi))) + (lo / (hi * -2.0));
end
code[lo_, hi_, x_] := N[(N[(x - lo), $MachinePrecision] / N[(hi - lo), $MachinePrecision]), $MachinePrecision]
code[lo_, hi_, x_] := N[(N[(N[(N[(x + x), $MachinePrecision] - lo), $MachinePrecision] * N[(2.0 * N[(0.25 / hi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(lo / N[(hi * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\frac{x - lo}{hi - lo}
\left(\left(x + x\right) - lo\right) \cdot \left(2 \cdot \frac{0.25}{hi}\right) + \frac{lo}{hi \cdot -2}

Error?

Try it out?

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation?

  1. Initial program 62.0

    \[\frac{x - lo}{hi - lo} \]
  2. Taylor expanded in hi around inf 52.0

    \[\leadsto \color{blue}{\frac{x - lo}{hi}} \]
  3. Applied egg-rr52.0

    \[\leadsto \color{blue}{\frac{1}{hi} \cdot \left(x - lo\right)} \]
  4. Applied egg-rr48.5

    \[\leadsto \color{blue}{\frac{x - \frac{lo}{2}}{hi} + \frac{lo}{hi \cdot -2}} \]
  5. Applied egg-rr64.0

    \[\leadsto \color{blue}{\left(2 \cdot \left(\left(x + x\right) - lo\right)\right) \cdot \frac{0.25}{hi}} + \frac{lo}{hi \cdot -2} \]
  6. Simplified48.6

    \[\leadsto \color{blue}{\left(\left(x + x\right) - lo\right) \cdot \left(2 \cdot \frac{0.25}{hi}\right)} + \frac{lo}{hi \cdot -2} \]
    Proof

    [Start]64.0

    \[ \left(2 \cdot \left(\left(x + x\right) - lo\right)\right) \cdot \frac{0.25}{hi} + \frac{lo}{hi \cdot -2} \]

    rational_best-simplify-1 [<=]64.0

    \[ \color{blue}{\frac{0.25}{hi} \cdot \left(2 \cdot \left(\left(x + x\right) - lo\right)\right)} + \frac{lo}{hi \cdot -2} \]

    rational_best-simplify-50 [=>]48.6

    \[ \color{blue}{\left(\left(x + x\right) - lo\right) \cdot \left(2 \cdot \frac{0.25}{hi}\right)} + \frac{lo}{hi \cdot -2} \]
  7. Final simplification48.6

    \[\leadsto \left(\left(x + x\right) - lo\right) \cdot \left(2 \cdot \frac{0.25}{hi}\right) + \frac{lo}{hi \cdot -2} \]

Alternatives

Alternative 1
Error48.5
Cost832
\[\frac{x - \frac{lo}{2}}{hi} + \frac{lo}{hi \cdot -2} \]
Alternative 2
Error48.5
Cost576
\[-\left(\frac{-lo}{lo + lo} - 0.5\right) \]
Alternative 3
Error48.5
Cost384
\[-\left(\frac{x}{lo} - 0.5\right) \]
Alternative 4
Error52.0
Cost320
\[\frac{x - lo}{hi} \]
Alternative 5
Error52.0
Cost256
\[\frac{lo}{-hi} \]
Alternative 6
Error52.0
Cost64
\[1 \]

Error

Reproduce?

herbie shell --seed 2023100 
(FPCore (lo hi x)
  :name "xlohi (overflows)"
  :precision binary64
  :pre (and (< lo -1e+308) (> hi 1e+308))
  (/ (- x lo) (- hi lo)))