Compound Interest

Percentage Accurate: 28.0% → 98.6%

Time: 21.0s

Alternatives: 14

Speedup: 38.0×

• could not determine a ground truth

  1. i = -2.0000000000000001e231
  2. n = 1.99999999999999997e77

Specification

Math

\[\begin{array}{l} \\ 100 \cdot \frac{{\left(1 + \frac{i}{n}\right)}^{n} - 1}{\frac{i}{n}} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (* 100.0 (/ (- (pow (+ 1.0 (/ i n)) n) 1.0) (/ i n))))

C

double code(double i, double n) {
	return 100.0 * ((pow((1.0 + (i / n)), n) - 1.0) / (i / n));
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    code = 100.0d0 * ((((1.0d0 + (i / n)) ** n) - 1.0d0) / (i / n))
end function

Java

public static double code(double i, double n) {
	return 100.0 * ((Math.pow((1.0 + (i / n)), n) - 1.0) / (i / n));
}

Python

def code(i, n):
	return 100.0 * ((math.pow((1.0 + (i / n)), n) - 1.0) / (i / n))

Julia

function code(i, n)
	return Float64(100.0 * Float64(Float64((Float64(1.0 + Float64(i / n)) ^ n) - 1.0) / Float64(i / n)))
end

MATLAB

function tmp = code(i, n)
	tmp = 100.0 * ((((1.0 + (i / n)) ^ n) - 1.0) / (i / n));
end

Wolfram

code[i_, n_] := N[(100.0 * N[(N[(N[Power[N[(1.0 + N[(i / n), $MachinePrecision]), $MachinePrecision], n], $MachinePrecision] - 1.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 28.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 100 \cdot \frac{{\left(1 + \frac{i}{n}\right)}^{n} - 1}{\frac{i}{n}} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (* 100.0 (/ (- (pow (+ 1.0 (/ i n)) n) 1.0) (/ i n))))

C

double code(double i, double n) {
	return 100.0 * ((pow((1.0 + (i / n)), n) - 1.0) / (i / n));
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    code = 100.0d0 * ((((1.0d0 + (i / n)) ** n) - 1.0d0) / (i / n))
end function

Java

public static double code(double i, double n) {
	return 100.0 * ((Math.pow((1.0 + (i / n)), n) - 1.0) / (i / n));
}

Python

def code(i, n):
	return 100.0 * ((math.pow((1.0 + (i / n)), n) - 1.0) / (i / n))

Julia

function code(i, n)
	return Float64(100.0 * Float64(Float64((Float64(1.0 + Float64(i / n)) ^ n) - 1.0) / Float64(i / n)))
end

MATLAB

function tmp = code(i, n)
	tmp = 100.0 * ((((1.0 + (i / n)) ^ n) - 1.0) / (i / n));
end

Wolfram

code[i_, n_] := N[(100.0 * N[(N[(N[Power[N[(1.0 + N[(i / n), $MachinePrecision]), $MachinePrecision], n], $MachinePrecision] - 1.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 98.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(1 + \frac{i}{n}\right)}^{n}\\ t_1 := \frac{t_0 + -1}{\frac{i}{n}}\\ \mathbf{if}\;t_1 \leq -4 \cdot 10^{-133}:\\ \;\;\;\;100 \cdot \left(t_0 \cdot \frac{n}{i} - \frac{n}{i}\right)\\ \mathbf{elif}\;t_1 \leq 0:\\ \;\;\;\;n \cdot \left(100 \cdot \frac{\mathsf{expm1}\left(n \cdot \mathsf{log1p}\left(\frac{i}{n}\right)\right)}{i}\right)\\ \mathbf{elif}\;t_1 \leq \infty:\\ \;\;\;\;\frac{t_0 \cdot 100 + -100}{\frac{i}{n}}\\ \mathbf{else}:\\ \;\;\;\;\frac{n}{0.01 + \frac{i}{n} \cdot 0.005}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (let* ((t_0 (pow (+ 1.0 (/ i n)) n)) (t_1 (/ (+ t_0 -1.0) (/ i n))))
   (if (<= t_1 -4e-133)
     (* 100.0 (- (* t_0 (/ n i)) (/ n i)))
     (if (<= t_1 0.0)
       (* n (* 100.0 (/ (expm1 (* n (log1p (/ i n)))) i)))
       (if (<= t_1 INFINITY)
         (/ (+ (* t_0 100.0) -100.0) (/ i n))
         (/ n (+ 0.01 (* (/ i n) 0.005))))))))

C

double code(double i, double n) {
	double t_0 = pow((1.0 + (i / n)), n);
	double t_1 = (t_0 + -1.0) / (i / n);
	double tmp;
	if (t_1 <= -4e-133) {
		tmp = 100.0 * ((t_0 * (n / i)) - (n / i));
	} else if (t_1 <= 0.0) {
		tmp = n * (100.0 * (expm1((n * log1p((i / n)))) / i));
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = ((t_0 * 100.0) + -100.0) / (i / n);
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Java

public static double code(double i, double n) {
	double t_0 = Math.pow((1.0 + (i / n)), n);
	double t_1 = (t_0 + -1.0) / (i / n);
	double tmp;
	if (t_1 <= -4e-133) {
		tmp = 100.0 * ((t_0 * (n / i)) - (n / i));
	} else if (t_1 <= 0.0) {
		tmp = n * (100.0 * (Math.expm1((n * Math.log1p((i / n)))) / i));
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = ((t_0 * 100.0) + -100.0) / (i / n);
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Python

def code(i, n):
	t_0 = math.pow((1.0 + (i / n)), n)
	t_1 = (t_0 + -1.0) / (i / n)
	tmp = 0
	if t_1 <= -4e-133:
		tmp = 100.0 * ((t_0 * (n / i)) - (n / i))
	elif t_1 <= 0.0:
		tmp = n * (100.0 * (math.expm1((n * math.log1p((i / n)))) / i))
	elif t_1 <= math.inf:
		tmp = ((t_0 * 100.0) + -100.0) / (i / n)
	else:
		tmp = n / (0.01 + ((i / n) * 0.005))
	return tmp

Julia

function code(i, n)
	t_0 = Float64(1.0 + Float64(i / n)) ^ n
	t_1 = Float64(Float64(t_0 + -1.0) / Float64(i / n))
	tmp = 0.0
	if (t_1 <= -4e-133)
		tmp = Float64(100.0 * Float64(Float64(t_0 * Float64(n / i)) - Float64(n / i)));
	elseif (t_1 <= 0.0)
		tmp = Float64(n * Float64(100.0 * Float64(expm1(Float64(n * log1p(Float64(i / n)))) / i)));
	elseif (t_1 <= Inf)
		tmp = Float64(Float64(Float64(t_0 * 100.0) + -100.0) / Float64(i / n));
	else
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i / n) * 0.005)));
	end
	return tmp
end

Wolfram

code[i_, n_] := Block[{t$95$0 = N[Power[N[(1.0 + N[(i / n), $MachinePrecision]), $MachinePrecision], n], $MachinePrecision]}, Block[{t$95$1 = N[(N[(t$95$0 + -1.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -4e-133], N[(100.0 * N[(N[(t$95$0 * N[(n / i), $MachinePrecision]), $MachinePrecision] - N[(n / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 0.0], N[(n * N[(100.0 * N[(N[(Exp[N[(n * N[Log[1 + N[(i / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]] - 1), $MachinePrecision] / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, Infinity], N[(N[(N[(t$95$0 * 100.0), $MachinePrecision] + -100.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision], N[(n / N[(0.01 + N[(N[(i / n), $MachinePrecision] * 0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 96.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(1 + \frac{i}{n}\right)}^{n}\\ t_1 := \frac{t_0 + -1}{\frac{i}{n}}\\ \mathbf{if}\;t_1 \leq 0:\\ \;\;\;\;\mathsf{expm1}\left(n \cdot \mathsf{log1p}\left(\frac{i}{n}\right)\right) \cdot \left(100 \cdot \frac{n}{i}\right)\\ \mathbf{elif}\;t_1 \leq \infty:\\ \;\;\;\;\frac{t_0 \cdot 100 + -100}{\frac{i}{n}}\\ \mathbf{else}:\\ \;\;\;\;\frac{n}{0.01 + \frac{i}{n} \cdot 0.005}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (let* ((t_0 (pow (+ 1.0 (/ i n)) n)) (t_1 (/ (+ t_0 -1.0) (/ i n))))
   (if (<= t_1 0.0)
     (* (expm1 (* n (log1p (/ i n)))) (* 100.0 (/ n i)))
     (if (<= t_1 INFINITY)
       (/ (+ (* t_0 100.0) -100.0) (/ i n))
       (/ n (+ 0.01 (* (/ i n) 0.005)))))))

C

double code(double i, double n) {
	double t_0 = pow((1.0 + (i / n)), n);
	double t_1 = (t_0 + -1.0) / (i / n);
	double tmp;
	if (t_1 <= 0.0) {
		tmp = expm1((n * log1p((i / n)))) * (100.0 * (n / i));
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = ((t_0 * 100.0) + -100.0) / (i / n);
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Java

public static double code(double i, double n) {
	double t_0 = Math.pow((1.0 + (i / n)), n);
	double t_1 = (t_0 + -1.0) / (i / n);
	double tmp;
	if (t_1 <= 0.0) {
		tmp = Math.expm1((n * Math.log1p((i / n)))) * (100.0 * (n / i));
	} else if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = ((t_0 * 100.0) + -100.0) / (i / n);
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Python

def code(i, n):
	t_0 = math.pow((1.0 + (i / n)), n)
	t_1 = (t_0 + -1.0) / (i / n)
	tmp = 0
	if t_1 <= 0.0:
		tmp = math.expm1((n * math.log1p((i / n)))) * (100.0 * (n / i))
	elif t_1 <= math.inf:
		tmp = ((t_0 * 100.0) + -100.0) / (i / n)
	else:
		tmp = n / (0.01 + ((i / n) * 0.005))
	return tmp

Julia

function code(i, n)
	t_0 = Float64(1.0 + Float64(i / n)) ^ n
	t_1 = Float64(Float64(t_0 + -1.0) / Float64(i / n))
	tmp = 0.0
	if (t_1 <= 0.0)
		tmp = Float64(expm1(Float64(n * log1p(Float64(i / n)))) * Float64(100.0 * Float64(n / i)));
	elseif (t_1 <= Inf)
		tmp = Float64(Float64(Float64(t_0 * 100.0) + -100.0) / Float64(i / n));
	else
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i / n) * 0.005)));
	end
	return tmp
end

Wolfram

code[i_, n_] := Block[{t$95$0 = N[Power[N[(1.0 + N[(i / n), $MachinePrecision]), $MachinePrecision], n], $MachinePrecision]}, Block[{t$95$1 = N[(N[(t$95$0 + -1.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, 0.0], N[(N[(Exp[N[(n * N[Log[1 + N[(i / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]] - 1), $MachinePrecision] * N[(100.0 * N[(n / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, Infinity], N[(N[(N[(t$95$0 * 100.0), $MachinePrecision] + -100.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision], N[(n / N[(0.01 + N[(N[(i / n), $MachinePrecision] * 0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 79.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 100 \cdot \frac{\mathsf{expm1}\left(i\right)}{\frac{i}{n}}\\ \mathbf{if}\;i \leq -3.6 \cdot 10^{-6}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;i \leq 0.0027:\\ \;\;\;\;\frac{n}{0.01 + \left(i \cdot -0.01\right) \cdot \left(0.5 - \frac{0.5}{n}\right)}\\ \mathbf{elif}\;i \leq 6.5 \cdot 10^{+212}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;200 \cdot \frac{{n}^{2}}{i}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (let* ((t_0 (* 100.0 (/ (expm1 i) (/ i n)))))
   (if (<= i -3.6e-6)
     t_0
     (if (<= i 0.0027)
       (/ n (+ 0.01 (* (* i -0.01) (- 0.5 (/ 0.5 n)))))
       (if (<= i 6.5e+212) t_0 (* 200.0 (/ (pow n 2.0) i)))))))

C

double code(double i, double n) {
	double t_0 = 100.0 * (expm1(i) / (i / n));
	double tmp;
	if (i <= -3.6e-6) {
		tmp = t_0;
	} else if (i <= 0.0027) {
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))));
	} else if (i <= 6.5e+212) {
		tmp = t_0;
	} else {
		tmp = 200.0 * (pow(n, 2.0) / i);
	}
	return tmp;
}

Java

public static double code(double i, double n) {
	double t_0 = 100.0 * (Math.expm1(i) / (i / n));
	double tmp;
	if (i <= -3.6e-6) {
		tmp = t_0;
	} else if (i <= 0.0027) {
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))));
	} else if (i <= 6.5e+212) {
		tmp = t_0;
	} else {
		tmp = 200.0 * (Math.pow(n, 2.0) / i);
	}
	return tmp;
}

Python

def code(i, n):
	t_0 = 100.0 * (math.expm1(i) / (i / n))
	tmp = 0
	if i <= -3.6e-6:
		tmp = t_0
	elif i <= 0.0027:
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))))
	elif i <= 6.5e+212:
		tmp = t_0
	else:
		tmp = 200.0 * (math.pow(n, 2.0) / i)
	return tmp

Julia

function code(i, n)
	t_0 = Float64(100.0 * Float64(expm1(i) / Float64(i / n)))
	tmp = 0.0
	if (i <= -3.6e-6)
		tmp = t_0;
	elseif (i <= 0.0027)
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i * -0.01) * Float64(0.5 - Float64(0.5 / n)))));
	elseif (i <= 6.5e+212)
		tmp = t_0;
	else
		tmp = Float64(200.0 * Float64((n ^ 2.0) / i));
	end
	return tmp
end

Wolfram

code[i_, n_] := Block[{t$95$0 = N[(100.0 * N[(N[(Exp[i] - 1), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[i, -3.6e-6], t$95$0, If[LessEqual[i, 0.0027], N[(n / N[(0.01 + N[(N[(i * -0.01), $MachinePrecision] * N[(0.5 - N[(0.5 / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[i, 6.5e+212], t$95$0, N[(200.0 * N[(N[Power[n, 2.0], $MachinePrecision] / i), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 87.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n \leq -1900 \lor \neg \left(n \leq 2.2 \cdot 10^{-10}\right):\\ \;\;\;\;n \cdot \left(100 \cdot \frac{\mathsf{expm1}\left(i\right)}{i}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{n}{0.01 + \frac{i}{n} \cdot 0.005}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (or (<= n -1900.0) (not (<= n 2.2e-10)))
   (* n (* 100.0 (/ (expm1 i) i)))
   (/ n (+ 0.01 (* (/ i n) 0.005)))))

C

double code(double i, double n) {
	double tmp;
	if ((n <= -1900.0) || !(n <= 2.2e-10)) {
		tmp = n * (100.0 * (expm1(i) / i));
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Java

public static double code(double i, double n) {
	double tmp;
	if ((n <= -1900.0) || !(n <= 2.2e-10)) {
		tmp = n * (100.0 * (Math.expm1(i) / i));
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if (n <= -1900.0) or not (n <= 2.2e-10):
		tmp = n * (100.0 * (math.expm1(i) / i))
	else:
		tmp = n / (0.01 + ((i / n) * 0.005))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if ((n <= -1900.0) || !(n <= 2.2e-10))
		tmp = Float64(n * Float64(100.0 * Float64(expm1(i) / i)));
	else
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i / n) * 0.005)));
	end
	return tmp
end

Wolfram

code[i_, n_] := If[Or[LessEqual[n, -1900.0], N[Not[LessEqual[n, 2.2e-10]], $MachinePrecision]], N[(n * N[(100.0 * N[(N[(Exp[i] - 1), $MachinePrecision] / i), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(n / N[(0.01 + N[(N[(i / n), $MachinePrecision] * 0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 87.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n \leq -1900 \lor \neg \left(n \leq 2.2 \cdot 10^{-10}\right):\\ \;\;\;\;100 \cdot \frac{n}{\frac{i}{\mathsf{expm1}\left(i\right)}}\\ \mathbf{else}:\\ \;\;\;\;\frac{n}{0.01 + \frac{i}{n} \cdot 0.005}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (or (<= n -1900.0) (not (<= n 2.2e-10)))
   (* 100.0 (/ n (/ i (expm1 i))))
   (/ n (+ 0.01 (* (/ i n) 0.005)))))

C

double code(double i, double n) {
	double tmp;
	if ((n <= -1900.0) || !(n <= 2.2e-10)) {
		tmp = 100.0 * (n / (i / expm1(i)));
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Java

public static double code(double i, double n) {
	double tmp;
	if ((n <= -1900.0) || !(n <= 2.2e-10)) {
		tmp = 100.0 * (n / (i / Math.expm1(i)));
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if (n <= -1900.0) or not (n <= 2.2e-10):
		tmp = 100.0 * (n / (i / math.expm1(i)))
	else:
		tmp = n / (0.01 + ((i / n) * 0.005))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if ((n <= -1900.0) || !(n <= 2.2e-10))
		tmp = Float64(100.0 * Float64(n / Float64(i / expm1(i))));
	else
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i / n) * 0.005)));
	end
	return tmp
end

Wolfram

code[i_, n_] := If[Or[LessEqual[n, -1900.0], N[Not[LessEqual[n, 2.2e-10]], $MachinePrecision]], N[(100.0 * N[(n / N[(i / N[(Exp[i] - 1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(n / N[(0.01 + N[(N[(i / n), $MachinePrecision] * 0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 71.4% accurate, 6.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n \leq -1.15 \cdot 10^{+209}:\\ \;\;\;\;\frac{100 \cdot \left(i \cdot n\right)}{i}\\ \mathbf{elif}\;n \leq 4.8 \cdot 10^{+31}:\\ \;\;\;\;\frac{n}{0.01 + \left(i \cdot -0.01\right) \cdot \left(0.5 - \frac{0.5}{n}\right)}\\ \mathbf{else}:\\ \;\;\;\;100 \cdot \left(n + 0.5 \cdot \left(i \cdot n\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (<= n -1.15e+209)
   (/ (* 100.0 (* i n)) i)
   (if (<= n 4.8e+31)
     (/ n (+ 0.01 (* (* i -0.01) (- 0.5 (/ 0.5 n)))))
     (* 100.0 (+ n (* 0.5 (* i n)))))))

C

double code(double i, double n) {
	double tmp;
	if (n <= -1.15e+209) {
		tmp = (100.0 * (i * n)) / i;
	} else if (n <= 4.8e+31) {
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))));
	} else {
		tmp = 100.0 * (n + (0.5 * (i * n)));
	}
	return tmp;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= (-1.15d+209)) then
        tmp = (100.0d0 * (i * n)) / i
    else if (n <= 4.8d+31) then
        tmp = n / (0.01d0 + ((i * (-0.01d0)) * (0.5d0 - (0.5d0 / n))))
    else
        tmp = 100.0d0 * (n + (0.5d0 * (i * n)))
    end if
    code = tmp
end function

Java

public static double code(double i, double n) {
	double tmp;
	if (n <= -1.15e+209) {
		tmp = (100.0 * (i * n)) / i;
	} else if (n <= 4.8e+31) {
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))));
	} else {
		tmp = 100.0 * (n + (0.5 * (i * n)));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if n <= -1.15e+209:
		tmp = (100.0 * (i * n)) / i
	elif n <= 4.8e+31:
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))))
	else:
		tmp = 100.0 * (n + (0.5 * (i * n)))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if (n <= -1.15e+209)
		tmp = Float64(Float64(100.0 * Float64(i * n)) / i);
	elseif (n <= 4.8e+31)
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i * -0.01) * Float64(0.5 - Float64(0.5 / n)))));
	else
		tmp = Float64(100.0 * Float64(n + Float64(0.5 * Float64(i * n))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(i, n)
	tmp = 0.0;
	if (n <= -1.15e+209)
		tmp = (100.0 * (i * n)) / i;
	elseif (n <= 4.8e+31)
		tmp = n / (0.01 + ((i * -0.01) * (0.5 - (0.5 / n))));
	else
		tmp = 100.0 * (n + (0.5 * (i * n)));
	end
	tmp_2 = tmp;
end

Wolfram

code[i_, n_] := If[LessEqual[n, -1.15e+209], N[(N[(100.0 * N[(i * n), $MachinePrecision]), $MachinePrecision] / i), $MachinePrecision], If[LessEqual[n, 4.8e+31], N[(n / N[(0.01 + N[(N[(i * -0.01), $MachinePrecision] * N[(0.5 - N[(0.5 / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(100.0 * N[(n + N[(0.5 * N[(i * n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 70.7% accurate, 8.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n \leq -4.5 \cdot 10^{+134} \lor \neg \left(n \leq 2.2 \cdot 10^{-10}\right):\\ \;\;\;\;100 \cdot \left(n + 0.5 \cdot \left(i \cdot n\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{n}{0.01 + \frac{i}{n} \cdot 0.005}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (or (<= n -4.5e+134) (not (<= n 2.2e-10)))
   (* 100.0 (+ n (* 0.5 (* i n))))
   (/ n (+ 0.01 (* (/ i n) 0.005)))))

C

double code(double i, double n) {
	double tmp;
	if ((n <= -4.5e+134) || !(n <= 2.2e-10)) {
		tmp = 100.0 * (n + (0.5 * (i * n)));
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((n <= (-4.5d+134)) .or. (.not. (n <= 2.2d-10))) then
        tmp = 100.0d0 * (n + (0.5d0 * (i * n)))
    else
        tmp = n / (0.01d0 + ((i / n) * 0.005d0))
    end if
    code = tmp
end function

Java

public static double code(double i, double n) {
	double tmp;
	if ((n <= -4.5e+134) || !(n <= 2.2e-10)) {
		tmp = 100.0 * (n + (0.5 * (i * n)));
	} else {
		tmp = n / (0.01 + ((i / n) * 0.005));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if (n <= -4.5e+134) or not (n <= 2.2e-10):
		tmp = 100.0 * (n + (0.5 * (i * n)))
	else:
		tmp = n / (0.01 + ((i / n) * 0.005))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if ((n <= -4.5e+134) || !(n <= 2.2e-10))
		tmp = Float64(100.0 * Float64(n + Float64(0.5 * Float64(i * n))));
	else
		tmp = Float64(n / Float64(0.01 + Float64(Float64(i / n) * 0.005)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(i, n)
	tmp = 0.0;
	if ((n <= -4.5e+134) || ~((n <= 2.2e-10)))
		tmp = 100.0 * (n + (0.5 * (i * n)));
	else
		tmp = n / (0.01 + ((i / n) * 0.005));
	end
	tmp_2 = tmp;
end

Wolfram

code[i_, n_] := If[Or[LessEqual[n, -4.5e+134], N[Not[LessEqual[n, 2.2e-10]], $MachinePrecision]], N[(100.0 * N[(n + N[(0.5 * N[(i * n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(n / N[(0.01 + N[(N[(i / n), $MachinePrecision] * 0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 63.3% accurate, 8.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n \leq -2.6 \cdot 10^{+204}:\\ \;\;\;\;\frac{100 \cdot \left(i \cdot n\right)}{i}\\ \mathbf{elif}\;n \leq 5.9 \cdot 10^{+30}:\\ \;\;\;\;\frac{n}{0.01 + i \cdot -0.005}\\ \mathbf{else}:\\ \;\;\;\;100 \cdot \left(n + 0.5 \cdot \left(i \cdot n\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (<= n -2.6e+204)
   (/ (* 100.0 (* i n)) i)
   (if (<= n 5.9e+30)
     (/ n (+ 0.01 (* i -0.005)))
     (* 100.0 (+ n (* 0.5 (* i n)))))))

C

double code(double i, double n) {
	double tmp;
	if (n <= -2.6e+204) {
		tmp = (100.0 * (i * n)) / i;
	} else if (n <= 5.9e+30) {
		tmp = n / (0.01 + (i * -0.005));
	} else {
		tmp = 100.0 * (n + (0.5 * (i * n)));
	}
	return tmp;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= (-2.6d+204)) then
        tmp = (100.0d0 * (i * n)) / i
    else if (n <= 5.9d+30) then
        tmp = n / (0.01d0 + (i * (-0.005d0)))
    else
        tmp = 100.0d0 * (n + (0.5d0 * (i * n)))
    end if
    code = tmp
end function

Java

public static double code(double i, double n) {
	double tmp;
	if (n <= -2.6e+204) {
		tmp = (100.0 * (i * n)) / i;
	} else if (n <= 5.9e+30) {
		tmp = n / (0.01 + (i * -0.005));
	} else {
		tmp = 100.0 * (n + (0.5 * (i * n)));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if n <= -2.6e+204:
		tmp = (100.0 * (i * n)) / i
	elif n <= 5.9e+30:
		tmp = n / (0.01 + (i * -0.005))
	else:
		tmp = 100.0 * (n + (0.5 * (i * n)))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if (n <= -2.6e+204)
		tmp = Float64(Float64(100.0 * Float64(i * n)) / i);
	elseif (n <= 5.9e+30)
		tmp = Float64(n / Float64(0.01 + Float64(i * -0.005)));
	else
		tmp = Float64(100.0 * Float64(n + Float64(0.5 * Float64(i * n))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(i, n)
	tmp = 0.0;
	if (n <= -2.6e+204)
		tmp = (100.0 * (i * n)) / i;
	elseif (n <= 5.9e+30)
		tmp = n / (0.01 + (i * -0.005));
	else
		tmp = 100.0 * (n + (0.5 * (i * n)));
	end
	tmp_2 = tmp;
end

Wolfram

code[i_, n_] := If[LessEqual[n, -2.6e+204], N[(N[(100.0 * N[(i * n), $MachinePrecision]), $MachinePrecision] / i), $MachinePrecision], If[LessEqual[n, 5.9e+30], N[(n / N[(0.01 + N[(i * -0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(100.0 * N[(n + N[(0.5 * N[(i * n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 56.3% accurate, 10.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;i \leq -4.5 \cdot 10^{-43} \lor \neg \left(i \leq 1.65 \cdot 10^{-51}\right):\\ \;\;\;\;100 \cdot \frac{i}{\frac{i}{n}}\\ \mathbf{else}:\\ \;\;\;\;100 \cdot \left(n + i \cdot -0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (or (<= i -4.5e-43) (not (<= i 1.65e-51)))
   (* 100.0 (/ i (/ i n)))
   (* 100.0 (+ n (* i -0.5)))))

C

double code(double i, double n) {
	double tmp;
	if ((i <= -4.5e-43) || !(i <= 1.65e-51)) {
		tmp = 100.0 * (i / (i / n));
	} else {
		tmp = 100.0 * (n + (i * -0.5));
	}
	return tmp;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((i <= (-4.5d-43)) .or. (.not. (i <= 1.65d-51))) then
        tmp = 100.0d0 * (i / (i / n))
    else
        tmp = 100.0d0 * (n + (i * (-0.5d0)))
    end if
    code = tmp
end function

Java

public static double code(double i, double n) {
	double tmp;
	if ((i <= -4.5e-43) || !(i <= 1.65e-51)) {
		tmp = 100.0 * (i / (i / n));
	} else {
		tmp = 100.0 * (n + (i * -0.5));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if (i <= -4.5e-43) or not (i <= 1.65e-51):
		tmp = 100.0 * (i / (i / n))
	else:
		tmp = 100.0 * (n + (i * -0.5))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if ((i <= -4.5e-43) || !(i <= 1.65e-51))
		tmp = Float64(100.0 * Float64(i / Float64(i / n)));
	else
		tmp = Float64(100.0 * Float64(n + Float64(i * -0.5)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(i, n)
	tmp = 0.0;
	if ((i <= -4.5e-43) || ~((i <= 1.65e-51)))
		tmp = 100.0 * (i / (i / n));
	else
		tmp = 100.0 * (n + (i * -0.5));
	end
	tmp_2 = tmp;
end

Wolfram

code[i_, n_] := If[Or[LessEqual[i, -4.5e-43], N[Not[LessEqual[i, 1.65e-51]], $MachinePrecision]], N[(100.0 * N[(i / N[(i / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(100.0 * N[(n + N[(i * -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 58.3% accurate, 10.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;i \leq -1 \cdot 10^{+18}:\\ \;\;\;\;100 \cdot \frac{i}{\frac{i}{n}}\\ \mathbf{elif}\;i \leq 4.5 \cdot 10^{+29}:\\ \;\;\;\;n \cdot 100\\ \mathbf{else}:\\ \;\;\;\;i \cdot \left(n \cdot 50 - 50\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (<= i -1e+18)
   (* 100.0 (/ i (/ i n)))
   (if (<= i 4.5e+29) (* n 100.0) (* i (- (* n 50.0) 50.0)))))

C

double code(double i, double n) {
	double tmp;
	if (i <= -1e+18) {
		tmp = 100.0 * (i / (i / n));
	} else if (i <= 4.5e+29) {
		tmp = n * 100.0;
	} else {
		tmp = i * ((n * 50.0) - 50.0);
	}
	return tmp;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: tmp
    if (i <= (-1d+18)) then
        tmp = 100.0d0 * (i / (i / n))
    else if (i <= 4.5d+29) then
        tmp = n * 100.0d0
    else
        tmp = i * ((n * 50.0d0) - 50.0d0)
    end if
    code = tmp
end function

Java

public static double code(double i, double n) {
	double tmp;
	if (i <= -1e+18) {
		tmp = 100.0 * (i / (i / n));
	} else if (i <= 4.5e+29) {
		tmp = n * 100.0;
	} else {
		tmp = i * ((n * 50.0) - 50.0);
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if i <= -1e+18:
		tmp = 100.0 * (i / (i / n))
	elif i <= 4.5e+29:
		tmp = n * 100.0
	else:
		tmp = i * ((n * 50.0) - 50.0)
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if (i <= -1e+18)
		tmp = Float64(100.0 * Float64(i / Float64(i / n)));
	elseif (i <= 4.5e+29)
		tmp = Float64(n * 100.0);
	else
		tmp = Float64(i * Float64(Float64(n * 50.0) - 50.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(i, n)
	tmp = 0.0;
	if (i <= -1e+18)
		tmp = 100.0 * (i / (i / n));
	elseif (i <= 4.5e+29)
		tmp = n * 100.0;
	else
		tmp = i * ((n * 50.0) - 50.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[i_, n_] := If[LessEqual[i, -1e+18], N[(100.0 * N[(i / N[(i / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[i, 4.5e+29], N[(n * 100.0), $MachinePrecision], N[(i * N[(N[(n * 50.0), $MachinePrecision] - 50.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 11: 59.3% accurate, 12.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;n \leq -4.2 \cdot 10^{+208}:\\ \;\;\;\;\frac{100 \cdot \left(i \cdot n\right)}{i}\\ \mathbf{else}:\\ \;\;\;\;\frac{n}{0.01 + i \cdot -0.005}\\ \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (if (<= n -4.2e+208) (/ (* 100.0 (* i n)) i) (/ n (+ 0.01 (* i -0.005)))))

C

double code(double i, double n) {
	double tmp;
	if (n <= -4.2e+208) {
		tmp = (100.0 * (i * n)) / i;
	} else {
		tmp = n / (0.01 + (i * -0.005));
	}
	return tmp;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= (-4.2d+208)) then
        tmp = (100.0d0 * (i * n)) / i
    else
        tmp = n / (0.01d0 + (i * (-0.005d0)))
    end if
    code = tmp
end function

Java

public static double code(double i, double n) {
	double tmp;
	if (n <= -4.2e+208) {
		tmp = (100.0 * (i * n)) / i;
	} else {
		tmp = n / (0.01 + (i * -0.005));
	}
	return tmp;
}

Python

def code(i, n):
	tmp = 0
	if n <= -4.2e+208:
		tmp = (100.0 * (i * n)) / i
	else:
		tmp = n / (0.01 + (i * -0.005))
	return tmp

Julia

function code(i, n)
	tmp = 0.0
	if (n <= -4.2e+208)
		tmp = Float64(Float64(100.0 * Float64(i * n)) / i);
	else
		tmp = Float64(n / Float64(0.01 + Float64(i * -0.005)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(i, n)
	tmp = 0.0;
	if (n <= -4.2e+208)
		tmp = (100.0 * (i * n)) / i;
	else
		tmp = n / (0.01 + (i * -0.005));
	end
	tmp_2 = tmp;
end

Wolfram

code[i_, n_] := If[LessEqual[n, -4.2e+208], N[(N[(100.0 * N[(i * n), $MachinePrecision]), $MachinePrecision] / i), $MachinePrecision], N[(n / N[(0.01 + N[(i * -0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 12: 57.9% accurate, 16.3× speedup

Math

\[\begin{array}{l} \\ \frac{n}{0.01 + i \cdot -0.005} \end{array} \]

FPCore

(FPCore (i n) :precision binary64 (/ n (+ 0.01 (* i -0.005))))

C

double code(double i, double n) {
	return n / (0.01 + (i * -0.005));
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    code = n / (0.01d0 + (i * (-0.005d0)))
end function

Java

public static double code(double i, double n) {
	return n / (0.01 + (i * -0.005));
}

Python

def code(i, n):
	return n / (0.01 + (i * -0.005))

Julia

function code(i, n)
	return Float64(n / Float64(0.01 + Float64(i * -0.005)))
end

MATLAB

function tmp = code(i, n)
	tmp = n / (0.01 + (i * -0.005));
end

Wolfram

code[i_, n_] := N[(n / N[(0.01 + N[(i * -0.005), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 13: 2.8% accurate, 38.0× speedup

Math

\[\begin{array}{l} \\ i \cdot -50 \end{array} \]

FPCore

(FPCore (i n) :precision binary64 (* i -50.0))

C

double code(double i, double n) {
	return i * -50.0;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    code = i * (-50.0d0)
end function

Java

public static double code(double i, double n) {
	return i * -50.0;
}

Python

def code(i, n):
	return i * -50.0

Julia

function code(i, n)
	return Float64(i * -50.0)
end

MATLAB

function tmp = code(i, n)
	tmp = i * -50.0;
end

Wolfram

code[i_, n_] := N[(i * -50.0), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 14: 49.4% accurate, 38.0× speedup

Math

\[\begin{array}{l} \\ n \cdot 100 \end{array} \]

FPCore

(FPCore (i n) :precision binary64 (* n 100.0))

C

double code(double i, double n) {
	return n * 100.0;
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    code = n * 100.0d0
end function

Java

public static double code(double i, double n) {
	return n * 100.0;
}

Python

def code(i, n):
	return n * 100.0

Julia

function code(i, n)
	return Float64(n * 100.0)
end

MATLAB

function tmp = code(i, n)
	tmp = n * 100.0;
end

Wolfram

code[i_, n_] := N[(n * 100.0), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Developer target: 34.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + \frac{i}{n}\\ 100 \cdot \frac{e^{n \cdot \begin{array}{l} \mathbf{if}\;t_0 = 1:\\ \;\;\;\;\frac{i}{n}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{i}{n} \cdot \log t_0}{\left(\frac{i}{n} + 1\right) - 1}\\ \end{array}} - 1}{\frac{i}{n}} \end{array} \end{array} \]

FPCore

(FPCore (i n)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ i n))))
   (*
    100.0
    (/
     (-
      (exp
       (*
        n
        (if (== t_0 1.0)
          (/ i n)
          (/ (* (/ i n) (log t_0)) (- (+ (/ i n) 1.0) 1.0)))))
      1.0)
     (/ i n)))))

C

double code(double i, double n) {
	double t_0 = 1.0 + (i / n);
	double tmp;
	if (t_0 == 1.0) {
		tmp = i / n;
	} else {
		tmp = ((i / n) * log(t_0)) / (((i / n) + 1.0) - 1.0);
	}
	return 100.0 * ((exp((n * tmp)) - 1.0) / (i / n));
}

Fortran

real(8) function code(i, n)
    real(8), intent (in) :: i
    real(8), intent (in) :: n
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 + (i / n)
    if (t_0 == 1.0d0) then
        tmp = i / n
    else
        tmp = ((i / n) * log(t_0)) / (((i / n) + 1.0d0) - 1.0d0)
    end if
    code = 100.0d0 * ((exp((n * tmp)) - 1.0d0) / (i / n))
end function

Java

public static double code(double i, double n) {
	double t_0 = 1.0 + (i / n);
	double tmp;
	if (t_0 == 1.0) {
		tmp = i / n;
	} else {
		tmp = ((i / n) * Math.log(t_0)) / (((i / n) + 1.0) - 1.0);
	}
	return 100.0 * ((Math.exp((n * tmp)) - 1.0) / (i / n));
}

Python

def code(i, n):
	t_0 = 1.0 + (i / n)
	tmp = 0
	if t_0 == 1.0:
		tmp = i / n
	else:
		tmp = ((i / n) * math.log(t_0)) / (((i / n) + 1.0) - 1.0)
	return 100.0 * ((math.exp((n * tmp)) - 1.0) / (i / n))

Julia

function code(i, n)
	t_0 = Float64(1.0 + Float64(i / n))
	tmp = 0.0
	if (t_0 == 1.0)
		tmp = Float64(i / n);
	else
		tmp = Float64(Float64(Float64(i / n) * log(t_0)) / Float64(Float64(Float64(i / n) + 1.0) - 1.0));
	end
	return Float64(100.0 * Float64(Float64(exp(Float64(n * tmp)) - 1.0) / Float64(i / n)))
end

MATLAB

function tmp_2 = code(i, n)
	t_0 = 1.0 + (i / n);
	tmp = 0.0;
	if (t_0 == 1.0)
		tmp = i / n;
	else
		tmp = ((i / n) * log(t_0)) / (((i / n) + 1.0) - 1.0);
	end
	tmp_2 = 100.0 * ((exp((n * tmp)) - 1.0) / (i / n));
end

Wolfram

code[i_, n_] := Block[{t$95$0 = N[(1.0 + N[(i / n), $MachinePrecision]), $MachinePrecision]}, N[(100.0 * N[(N[(N[Exp[N[(n * If[Equal[t$95$0, 1.0], N[(i / n), $MachinePrecision], N[(N[(N[(i / n), $MachinePrecision] * N[Log[t$95$0], $MachinePrecision]), $MachinePrecision] / N[(N[(N[(i / n), $MachinePrecision] + 1.0), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]], $MachinePrecision] - 1.0), $MachinePrecision] / N[(i / n), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2023347 
(FPCore (i n)
  :name "Compound Interest"
  :precision binary64

  :herbie-target
  (* 100.0 (/ (- (exp (* n (if (== (+ 1.0 (/ i n)) 1.0) (/ i n) (/ (* (/ i n) (log (+ 1.0 (/ i n)))) (- (+ (/ i n) 1.0) 1.0))))) 1.0) (/ i n)))

  (* 100.0 (/ (- (pow (+ 1.0 (/ i n)) n) 1.0) (/ i n))))