2cbrt (problem 3.3.4)

Percentage Accurate: 6.8% → 98.2%
Time: 7.4s
Alternatives: 5
Speedup: 1.9×

Specification

?
\[x > 1 \land x < 10^{+308}\]
\[\begin{array}{l} \\ \sqrt[3]{x + 1} - \sqrt[3]{x} \end{array} \]
(FPCore (x) :precision binary64 (- (cbrt (+ x 1.0)) (cbrt x)))
double code(double x) {
	return cbrt((x + 1.0)) - cbrt(x);
}
public static double code(double x) {
	return Math.cbrt((x + 1.0)) - Math.cbrt(x);
}
function code(x)
	return Float64(cbrt(Float64(x + 1.0)) - cbrt(x))
end
code[x_] := N[(N[Power[N[(x + 1.0), $MachinePrecision], 1/3], $MachinePrecision] - N[Power[x, 1/3], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}

\\
\sqrt[3]{x + 1} - \sqrt[3]{x}
\end{array}

Sampling outcomes in binary64 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 5 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 6.8% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \sqrt[3]{x + 1} - \sqrt[3]{x} \end{array} \]
(FPCore (x) :precision binary64 (- (cbrt (+ x 1.0)) (cbrt x)))
double code(double x) {
	return cbrt((x + 1.0)) - cbrt(x);
}
public static double code(double x) {
	return Math.cbrt((x + 1.0)) - Math.cbrt(x);
}
function code(x)
	return Float64(cbrt(Float64(x + 1.0)) - cbrt(x))
end
code[x_] := N[(N[Power[N[(x + 1.0), $MachinePrecision], 1/3], $MachinePrecision] - N[Power[x, 1/3], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}

\\
\sqrt[3]{x + 1} - \sqrt[3]{x}
\end{array}

Alternative 1: 98.2% accurate, 1.5× speedup?

\[\begin{array}{l} \\ \frac{\sqrt[3]{x}}{x} \cdot \frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x} \end{array} \]
(FPCore (x)
 :precision binary64
 (* (/ (cbrt x) x) (/ (fma 0.3333333333333333 x -0.1111111111111111) x)))
double code(double x) {
	return (cbrt(x) / x) * (fma(0.3333333333333333, x, -0.1111111111111111) / x);
}
function code(x)
	return Float64(Float64(cbrt(x) / x) * Float64(fma(0.3333333333333333, x, -0.1111111111111111) / x))
end
code[x_] := N[(N[(N[Power[x, 1/3], $MachinePrecision] / x), $MachinePrecision] * N[(N[(0.3333333333333333 * x + -0.1111111111111111), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}

\\
\frac{\sqrt[3]{x}}{x} \cdot \frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}
\end{array}
Derivation
  1. Initial program 6.6%

    \[\sqrt[3]{x + 1} - \sqrt[3]{x} \]
  2. Add Preprocessing
  3. Taylor expanded in x around inf

    \[\leadsto \color{blue}{\frac{\frac{-1}{9} \cdot \sqrt[3]{x} + \frac{1}{3} \cdot \sqrt[3]{{x}^{4}}}{{x}^{2}}} \]
  4. Step-by-step derivation
    1. lower-/.f64N/A

      \[\leadsto \color{blue}{\frac{\frac{-1}{9} \cdot \sqrt[3]{x} + \frac{1}{3} \cdot \sqrt[3]{{x}^{4}}}{{x}^{2}}} \]
    2. +-commutativeN/A

      \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \sqrt[3]{{x}^{4}} + \frac{-1}{9} \cdot \sqrt[3]{x}}}{{x}^{2}} \]
    3. *-commutativeN/A

      \[\leadsto \frac{\color{blue}{\sqrt[3]{{x}^{4}} \cdot \frac{1}{3}} + \frac{-1}{9} \cdot \sqrt[3]{x}}{{x}^{2}} \]
    4. lower-fma.f64N/A

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}}{{x}^{2}} \]
    5. metadata-evalN/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{\color{blue}{\left(2 \cdot 2\right)}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
    6. pow-sqrN/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{2} \cdot {x}^{2}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
    7. lower-cbrt.f64N/A

      \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt[3]{{x}^{2} \cdot {x}^{2}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
    8. pow-sqrN/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{\left(2 \cdot 2\right)}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
    9. metadata-evalN/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{\color{blue}{4}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
    10. lower-pow.f64N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{4}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
    11. lower-*.f64N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \color{blue}{\frac{-1}{9} \cdot \sqrt[3]{x}}\right)}{{x}^{2}} \]
    12. lower-cbrt.f64N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \color{blue}{\sqrt[3]{x}}\right)}{{x}^{2}} \]
    13. unpow2N/A

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{\color{blue}{x \cdot x}} \]
    14. lower-*.f6424.9

      \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)}{\color{blue}{x \cdot x}} \]
  5. Applied rewrites24.9%

    \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)}{x \cdot x}} \]
  6. Step-by-step derivation
    1. Applied rewrites76.5%

      \[\leadsto \frac{-1}{\color{blue}{\frac{x}{\mathsf{fma}\left(\sqrt[3]{x} \cdot x, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)} \cdot \left(-x\right)}} \]
    2. Step-by-step derivation
      1. Applied rewrites98.7%

        \[\leadsto \frac{\sqrt[3]{x}}{x} \cdot \color{blue}{\frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}} \]
      2. Add Preprocessing

      Alternative 2: 98.2% accurate, 1.5× speedup?

      \[\begin{array}{l} \\ \sqrt[3]{x} \cdot \frac{\frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}}{x} \end{array} \]
      (FPCore (x)
       :precision binary64
       (* (cbrt x) (/ (/ (fma 0.3333333333333333 x -0.1111111111111111) x) x)))
      double code(double x) {
      	return cbrt(x) * ((fma(0.3333333333333333, x, -0.1111111111111111) / x) / x);
      }
      
      function code(x)
      	return Float64(cbrt(x) * Float64(Float64(fma(0.3333333333333333, x, -0.1111111111111111) / x) / x))
      end
      
      code[x_] := N[(N[Power[x, 1/3], $MachinePrecision] * N[(N[(N[(0.3333333333333333 * x + -0.1111111111111111), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]
      
      \begin{array}{l}
      
      \\
      \sqrt[3]{x} \cdot \frac{\frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}}{x}
      \end{array}
      
      Derivation
      1. Initial program 6.6%

        \[\sqrt[3]{x + 1} - \sqrt[3]{x} \]
      2. Add Preprocessing
      3. Taylor expanded in x around inf

        \[\leadsto \color{blue}{\frac{\frac{-1}{9} \cdot \sqrt[3]{x} + \frac{1}{3} \cdot \sqrt[3]{{x}^{4}}}{{x}^{2}}} \]
      4. Step-by-step derivation
        1. lower-/.f64N/A

          \[\leadsto \color{blue}{\frac{\frac{-1}{9} \cdot \sqrt[3]{x} + \frac{1}{3} \cdot \sqrt[3]{{x}^{4}}}{{x}^{2}}} \]
        2. +-commutativeN/A

          \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \sqrt[3]{{x}^{4}} + \frac{-1}{9} \cdot \sqrt[3]{x}}}{{x}^{2}} \]
        3. *-commutativeN/A

          \[\leadsto \frac{\color{blue}{\sqrt[3]{{x}^{4}} \cdot \frac{1}{3}} + \frac{-1}{9} \cdot \sqrt[3]{x}}{{x}^{2}} \]
        4. lower-fma.f64N/A

          \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}}{{x}^{2}} \]
        5. metadata-evalN/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{\color{blue}{\left(2 \cdot 2\right)}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
        6. pow-sqrN/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{2} \cdot {x}^{2}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
        7. lower-cbrt.f64N/A

          \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt[3]{{x}^{2} \cdot {x}^{2}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
        8. pow-sqrN/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{\left(2 \cdot 2\right)}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
        9. metadata-evalN/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{\color{blue}{4}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
        10. lower-pow.f64N/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{4}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
        11. lower-*.f64N/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \color{blue}{\frac{-1}{9} \cdot \sqrt[3]{x}}\right)}{{x}^{2}} \]
        12. lower-cbrt.f64N/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \color{blue}{\sqrt[3]{x}}\right)}{{x}^{2}} \]
        13. unpow2N/A

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{\color{blue}{x \cdot x}} \]
        14. lower-*.f6424.9

          \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)}{\color{blue}{x \cdot x}} \]
      5. Applied rewrites24.9%

        \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)}{x \cdot x}} \]
      6. Step-by-step derivation
        1. Applied rewrites76.5%

          \[\leadsto \frac{-1}{\color{blue}{\frac{x}{\mathsf{fma}\left(\sqrt[3]{x} \cdot x, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)} \cdot \left(-x\right)}} \]
        2. Step-by-step derivation
          1. Applied rewrites98.7%

            \[\leadsto \frac{\sqrt[3]{x}}{x} \cdot \color{blue}{\frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}} \]
          2. Step-by-step derivation
            1. Applied rewrites98.7%

              \[\leadsto \sqrt[3]{x} \cdot \color{blue}{\frac{\frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}}{x}} \]
            2. Add Preprocessing

            Alternative 3: 97.2% accurate, 1.8× speedup?

            \[\begin{array}{l} \\ \frac{\sqrt[3]{x}}{x} \cdot 0.3333333333333333 \end{array} \]
            (FPCore (x) :precision binary64 (* (/ (cbrt x) x) 0.3333333333333333))
            double code(double x) {
            	return (cbrt(x) / x) * 0.3333333333333333;
            }
            
            public static double code(double x) {
            	return (Math.cbrt(x) / x) * 0.3333333333333333;
            }
            
            function code(x)
            	return Float64(Float64(cbrt(x) / x) * 0.3333333333333333)
            end
            
            code[x_] := N[(N[(N[Power[x, 1/3], $MachinePrecision] / x), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]
            
            \begin{array}{l}
            
            \\
            \frac{\sqrt[3]{x}}{x} \cdot 0.3333333333333333
            \end{array}
            
            Derivation
            1. Initial program 6.6%

              \[\sqrt[3]{x + 1} - \sqrt[3]{x} \]
            2. Add Preprocessing
            3. Taylor expanded in x around inf

              \[\leadsto \color{blue}{\frac{\frac{-1}{9} \cdot \sqrt[3]{x} + \frac{1}{3} \cdot \sqrt[3]{{x}^{4}}}{{x}^{2}}} \]
            4. Step-by-step derivation
              1. lower-/.f64N/A

                \[\leadsto \color{blue}{\frac{\frac{-1}{9} \cdot \sqrt[3]{x} + \frac{1}{3} \cdot \sqrt[3]{{x}^{4}}}{{x}^{2}}} \]
              2. +-commutativeN/A

                \[\leadsto \frac{\color{blue}{\frac{1}{3} \cdot \sqrt[3]{{x}^{4}} + \frac{-1}{9} \cdot \sqrt[3]{x}}}{{x}^{2}} \]
              3. *-commutativeN/A

                \[\leadsto \frac{\color{blue}{\sqrt[3]{{x}^{4}} \cdot \frac{1}{3}} + \frac{-1}{9} \cdot \sqrt[3]{x}}{{x}^{2}} \]
              4. lower-fma.f64N/A

                \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}}{{x}^{2}} \]
              5. metadata-evalN/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{\color{blue}{\left(2 \cdot 2\right)}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
              6. pow-sqrN/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{2} \cdot {x}^{2}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
              7. lower-cbrt.f64N/A

                \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{\sqrt[3]{{x}^{2} \cdot {x}^{2}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
              8. pow-sqrN/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{\left(2 \cdot 2\right)}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
              9. metadata-evalN/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{\color{blue}{4}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
              10. lower-pow.f64N/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{\color{blue}{{x}^{4}}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{{x}^{2}} \]
              11. lower-*.f64N/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \color{blue}{\frac{-1}{9} \cdot \sqrt[3]{x}}\right)}{{x}^{2}} \]
              12. lower-cbrt.f64N/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \color{blue}{\sqrt[3]{x}}\right)}{{x}^{2}} \]
              13. unpow2N/A

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, \frac{1}{3}, \frac{-1}{9} \cdot \sqrt[3]{x}\right)}{\color{blue}{x \cdot x}} \]
              14. lower-*.f6424.9

                \[\leadsto \frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)}{\color{blue}{x \cdot x}} \]
            5. Applied rewrites24.9%

              \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\sqrt[3]{{x}^{4}}, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)}{x \cdot x}} \]
            6. Step-by-step derivation
              1. Applied rewrites76.5%

                \[\leadsto \frac{-1}{\color{blue}{\frac{x}{\mathsf{fma}\left(\sqrt[3]{x} \cdot x, 0.3333333333333333, -0.1111111111111111 \cdot \sqrt[3]{x}\right)} \cdot \left(-x\right)}} \]
              2. Step-by-step derivation
                1. Applied rewrites98.7%

                  \[\leadsto \frac{\sqrt[3]{x}}{x} \cdot \color{blue}{\frac{\mathsf{fma}\left(0.3333333333333333, x, -0.1111111111111111\right)}{x}} \]
                2. Taylor expanded in x around inf

                  \[\leadsto \frac{\sqrt[3]{x}}{x} \cdot \frac{1}{3} \]
                3. Step-by-step derivation
                  1. Applied rewrites97.5%

                    \[\leadsto \frac{\sqrt[3]{x}}{x} \cdot 0.3333333333333333 \]
                  2. Add Preprocessing

                  Alternative 4: 88.9% accurate, 1.9× speedup?

                  \[\begin{array}{l} \\ {x}^{-0.6666666666666666} \cdot 0.3333333333333333 \end{array} \]
                  (FPCore (x)
                   :precision binary64
                   (* (pow x -0.6666666666666666) 0.3333333333333333))
                  double code(double x) {
                  	return pow(x, -0.6666666666666666) * 0.3333333333333333;
                  }
                  
                  real(8) function code(x)
                      real(8), intent (in) :: x
                      code = (x ** (-0.6666666666666666d0)) * 0.3333333333333333d0
                  end function
                  
                  public static double code(double x) {
                  	return Math.pow(x, -0.6666666666666666) * 0.3333333333333333;
                  }
                  
                  def code(x):
                  	return math.pow(x, -0.6666666666666666) * 0.3333333333333333
                  
                  function code(x)
                  	return Float64((x ^ -0.6666666666666666) * 0.3333333333333333)
                  end
                  
                  function tmp = code(x)
                  	tmp = (x ^ -0.6666666666666666) * 0.3333333333333333;
                  end
                  
                  code[x_] := N[(N[Power[x, -0.6666666666666666], $MachinePrecision] * 0.3333333333333333), $MachinePrecision]
                  
                  \begin{array}{l}
                  
                  \\
                  {x}^{-0.6666666666666666} \cdot 0.3333333333333333
                  \end{array}
                  
                  Derivation
                  1. Initial program 6.6%

                    \[\sqrt[3]{x + 1} - \sqrt[3]{x} \]
                  2. Add Preprocessing
                  3. Taylor expanded in x around inf

                    \[\leadsto \color{blue}{\frac{1}{3} \cdot \sqrt[3]{\frac{1}{{x}^{2}}}} \]
                  4. Step-by-step derivation
                    1. *-commutativeN/A

                      \[\leadsto \color{blue}{\sqrt[3]{\frac{1}{{x}^{2}}} \cdot \frac{1}{3}} \]
                    2. lower-*.f64N/A

                      \[\leadsto \color{blue}{\sqrt[3]{\frac{1}{{x}^{2}}} \cdot \frac{1}{3}} \]
                    3. metadata-evalN/A

                      \[\leadsto \sqrt[3]{\frac{\color{blue}{-1 \cdot -1}}{{x}^{2}}} \cdot \frac{1}{3} \]
                    4. associate-*r/N/A

                      \[\leadsto \sqrt[3]{\color{blue}{-1 \cdot \frac{-1}{{x}^{2}}}} \cdot \frac{1}{3} \]
                    5. lower-cbrt.f64N/A

                      \[\leadsto \color{blue}{\sqrt[3]{-1 \cdot \frac{-1}{{x}^{2}}}} \cdot \frac{1}{3} \]
                    6. unpow2N/A

                      \[\leadsto \sqrt[3]{-1 \cdot \frac{-1}{\color{blue}{x \cdot x}}} \cdot \frac{1}{3} \]
                    7. associate-/r*N/A

                      \[\leadsto \sqrt[3]{-1 \cdot \color{blue}{\frac{\frac{-1}{x}}{x}}} \cdot \frac{1}{3} \]
                    8. associate-*r/N/A

                      \[\leadsto \sqrt[3]{\color{blue}{\frac{-1 \cdot \frac{-1}{x}}{x}}} \cdot \frac{1}{3} \]
                    9. lower-/.f64N/A

                      \[\leadsto \sqrt[3]{\color{blue}{\frac{-1 \cdot \frac{-1}{x}}{x}}} \cdot \frac{1}{3} \]
                    10. associate-*r/N/A

                      \[\leadsto \sqrt[3]{\frac{\color{blue}{\frac{-1 \cdot -1}{x}}}{x}} \cdot \frac{1}{3} \]
                    11. metadata-evalN/A

                      \[\leadsto \sqrt[3]{\frac{\frac{\color{blue}{1}}{x}}{x}} \cdot \frac{1}{3} \]
                    12. lower-/.f6452.9

                      \[\leadsto \sqrt[3]{\frac{\color{blue}{\frac{1}{x}}}{x}} \cdot 0.3333333333333333 \]
                  5. Applied rewrites52.9%

                    \[\leadsto \color{blue}{\sqrt[3]{\frac{\frac{1}{x}}{x}} \cdot 0.3333333333333333} \]
                  6. Step-by-step derivation
                    1. Applied rewrites89.1%

                      \[\leadsto {x}^{-0.6666666666666666} \cdot 0.3333333333333333 \]
                    2. Add Preprocessing

                    Alternative 5: 1.8% accurate, 2.0× speedup?

                    \[\begin{array}{l} \\ 1 - \sqrt[3]{x} \end{array} \]
                    (FPCore (x) :precision binary64 (- 1.0 (cbrt x)))
                    double code(double x) {
                    	return 1.0 - cbrt(x);
                    }
                    
                    public static double code(double x) {
                    	return 1.0 - Math.cbrt(x);
                    }
                    
                    function code(x)
                    	return Float64(1.0 - cbrt(x))
                    end
                    
                    code[x_] := N[(1.0 - N[Power[x, 1/3], $MachinePrecision]), $MachinePrecision]
                    
                    \begin{array}{l}
                    
                    \\
                    1 - \sqrt[3]{x}
                    \end{array}
                    
                    Derivation
                    1. Initial program 6.6%

                      \[\sqrt[3]{x + 1} - \sqrt[3]{x} \]
                    2. Add Preprocessing
                    3. Taylor expanded in x around 0

                      \[\leadsto \color{blue}{1} - \sqrt[3]{x} \]
                    4. Step-by-step derivation
                      1. Applied rewrites1.8%

                        \[\leadsto \color{blue}{1} - \sqrt[3]{x} \]
                      2. Add Preprocessing

                      Developer Target 1: 98.5% accurate, 0.3× speedup?

                      \[\begin{array}{l} \\ \begin{array}{l} t_0 := \sqrt[3]{x + 1}\\ \frac{1}{\left(t\_0 \cdot t\_0 + \sqrt[3]{x} \cdot t\_0\right) + \sqrt[3]{x} \cdot \sqrt[3]{x}} \end{array} \end{array} \]
                      (FPCore (x)
                       :precision binary64
                       (let* ((t_0 (cbrt (+ x 1.0))))
                         (/ 1.0 (+ (+ (* t_0 t_0) (* (cbrt x) t_0)) (* (cbrt x) (cbrt x))))))
                      double code(double x) {
                      	double t_0 = cbrt((x + 1.0));
                      	return 1.0 / (((t_0 * t_0) + (cbrt(x) * t_0)) + (cbrt(x) * cbrt(x)));
                      }
                      
                      public static double code(double x) {
                      	double t_0 = Math.cbrt((x + 1.0));
                      	return 1.0 / (((t_0 * t_0) + (Math.cbrt(x) * t_0)) + (Math.cbrt(x) * Math.cbrt(x)));
                      }
                      
                      function code(x)
                      	t_0 = cbrt(Float64(x + 1.0))
                      	return Float64(1.0 / Float64(Float64(Float64(t_0 * t_0) + Float64(cbrt(x) * t_0)) + Float64(cbrt(x) * cbrt(x))))
                      end
                      
                      code[x_] := Block[{t$95$0 = N[Power[N[(x + 1.0), $MachinePrecision], 1/3], $MachinePrecision]}, N[(1.0 / N[(N[(N[(t$95$0 * t$95$0), $MachinePrecision] + N[(N[Power[x, 1/3], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(N[Power[x, 1/3], $MachinePrecision] * N[Power[x, 1/3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
                      
                      \begin{array}{l}
                      
                      \\
                      \begin{array}{l}
                      t_0 := \sqrt[3]{x + 1}\\
                      \frac{1}{\left(t\_0 \cdot t\_0 + \sqrt[3]{x} \cdot t\_0\right) + \sqrt[3]{x} \cdot \sqrt[3]{x}}
                      \end{array}
                      \end{array}
                      

                      Reproduce

                      ?
                      herbie shell --seed 2024307 
                      (FPCore (x)
                        :name "2cbrt (problem 3.3.4)"
                        :precision binary64
                        :pre (and (> x 1.0) (< x 1e+308))
                      
                        :alt
                        (! :herbie-platform default (/ 1 (+ (* (cbrt (+ x 1)) (cbrt (+ x 1))) (* (cbrt x) (cbrt (+ x 1))) (* (cbrt x) (cbrt x)))))
                      
                        (- (cbrt (+ x 1.0)) (cbrt x)))