Logarithm of a Bigdecimal

Logarithm of a BigDecimal

Java Number Cruncher: The Java Programmer's Guide to Numerical Computing provides a solution using Newton's Method. Source code from the book is available here. The following has been taken from chapter 12.5 Big Decimal Functions (p330 & p331):

/**
 * Compute the natural logarithm of x to a given scale, x > 0.
 */
public static BigDecimal ln(BigDecimal x, int scale)
{
    // Check that x > 0.
    if (x.signum() <= 0) {
        throw new IllegalArgumentException("x <= 0");
    }

    // The number of digits to the left of the decimal point.
    int magnitude = x.toString().length() - x.scale() - 1;

    if (magnitude < 3) {
        return lnNewton(x, scale);
    }

    // Compute magnitude*ln(x^(1/magnitude)).
    else {

        // x^(1/magnitude)
        BigDecimal root = intRoot(x, magnitude, scale);

        // ln(x^(1/magnitude))
        BigDecimal lnRoot = lnNewton(root, scale);

        // magnitude*ln(x^(1/magnitude))
        return BigDecimal.valueOf(magnitude).multiply(lnRoot)
                    .setScale(scale, BigDecimal.ROUND_HALF_EVEN);
    }
}

/**
 * Compute the natural logarithm of x to a given scale, x > 0.
 * Use Newton's algorithm.
 */
private static BigDecimal lnNewton(BigDecimal x, int scale)
{
    int        sp1 = scale + 1;
    BigDecimal n   = x;
    BigDecimal term;

    // Convergence tolerance = 5*(10^-(scale+1))
    BigDecimal tolerance = BigDecimal.valueOf(5)
                                        .movePointLeft(sp1);

    // Loop until the approximations converge
    // (two successive approximations are within the tolerance).
    do {

        // e^x
        BigDecimal eToX = exp(x, sp1);

        // (e^x - n)/e^x
        term = eToX.subtract(n)
                    .divide(eToX, sp1, BigDecimal.ROUND_DOWN);

        // x - (e^x - n)/e^x
        x = x.subtract(term);

        Thread.yield();
    } while (term.compareTo(tolerance) > 0);

    return x.setScale(scale, BigDecimal.ROUND_HALF_EVEN);
}

/**
 * Compute the integral root of x to a given scale, x >= 0.
 * Use Newton's algorithm.
 * @param x the value of x
 * @param index the integral root value
 * @param scale the desired scale of the result
 * @return the result value
 */
public static BigDecimal intRoot(BigDecimal x, long index,
                                 int scale)
{
    // Check that x >= 0.
    if (x.signum() < 0) {
        throw new IllegalArgumentException("x < 0");
    }

    int        sp1 = scale + 1;
    BigDecimal n   = x;
    BigDecimal i   = BigDecimal.valueOf(index);
    BigDecimal im1 = BigDecimal.valueOf(index-1);
    BigDecimal tolerance = BigDecimal.valueOf(5)
                                        .movePointLeft(sp1);
    BigDecimal xPrev;

    // The initial approximation is x/index.
    x = x.divide(i, scale, BigDecimal.ROUND_HALF_EVEN);

    // Loop until the approximations converge
    // (two successive approximations are equal after rounding).
    do {
        // x^(index-1)
        BigDecimal xToIm1 = intPower(x, index-1, sp1);

        // x^index
        BigDecimal xToI =
                x.multiply(xToIm1)
                    .setScale(sp1, BigDecimal.ROUND_HALF_EVEN);

        // n + (index-1)*(x^index)
        BigDecimal numerator =
                n.add(im1.multiply(xToI))
                    .setScale(sp1, BigDecimal.ROUND_HALF_EVEN);

        // (index*(x^(index-1))
        BigDecimal denominator =
                i.multiply(xToIm1)
                    .setScale(sp1, BigDecimal.ROUND_HALF_EVEN);

        // x = (n + (index-1)*(x^index)) / (index*(x^(index-1)))
        xPrev = x;
        x = numerator
                .divide(denominator, sp1, BigDecimal.ROUND_DOWN);

        Thread.yield();
    } while (x.subtract(xPrev).abs().compareTo(tolerance) > 0);

    return x;
}

/**
 * Compute e^x to a given scale.
 * Break x into its whole and fraction parts and
 * compute (e^(1 + fraction/whole))^whole using Taylor's formula.
 * @param x the value of x
 * @param scale the desired scale of the result
 * @return the result value
 */
public static BigDecimal exp(BigDecimal x, int scale)
{
    // e^0 = 1
    if (x.signum() == 0) {
        return BigDecimal.valueOf(1);
    }

    // If x is negative, return 1/(e^-x).
    else if (x.signum() == -1) {
        return BigDecimal.valueOf(1)
                    .divide(exp(x.negate(), scale), scale,
                            BigDecimal.ROUND_HALF_EVEN);
    }

    // Compute the whole part of x.
    BigDecimal xWhole = x.setScale(0, BigDecimal.ROUND_DOWN);

    // If there isn't a whole part, compute and return e^x.
    if (xWhole.signum() == 0) return expTaylor(x, scale);

    // Compute the fraction part of x.
    BigDecimal xFraction = x.subtract(xWhole);

    // z = 1 + fraction/whole
    BigDecimal z = BigDecimal.valueOf(1)
                        .add(xFraction.divide(
                                xWhole, scale,
                                BigDecimal.ROUND_HALF_EVEN));

    // t = e^z
    BigDecimal t = expTaylor(z, scale);

    BigDecimal maxLong = BigDecimal.valueOf(Long.MAX_VALUE);
    BigDecimal result  = BigDecimal.valueOf(1);

    // Compute and return t^whole using intPower().
    // If whole > Long.MAX_VALUE, then first compute products
    // of e^Long.MAX_VALUE.
    while (xWhole.compareTo(maxLong) >= 0) {
        result = result.multiply(
                            intPower(t, Long.MAX_VALUE, scale))
                    .setScale(scale, BigDecimal.ROUND_HALF_EVEN);
        xWhole = xWhole.subtract(maxLong);

        Thread.yield();
    }
    return result.multiply(intPower(t, xWhole.longValue(), scale))
                    .setScale(scale, BigDecimal.ROUND_HALF_EVEN);
}

Logarithm Algorithm

Use this identity:

log_b(n) = log_e(n) / log_e(b)

Where log can be a logarithm function in any base, n is the number and b is the base. For example, in Java this will find the base-2 logarithm of 256:

Math.log(256) / Math.log(2)
=> 8.0

Math.log() uses base e, by the way. And there's also Math.log10(), which uses base 10.

Aproximation of log(10^k)

Use the fact that

log(10^k) = k*log(10)

So:

System.out.println(10000 * Math.log(10));

Prints:

23025.850929940458

Logarithm of a BigInt

In case you don't want to return a BigInt, then the following might work for you too:

function log10(bigint) {
  if (bigint < 0) return NaN;
  const s = bigint.toString(10);

  return s.length + Math.log10("0." + s.substring(0, 15))
}

function log(bigint) {
  return log10(bigint) * Math.log(10);
}

function natlog(bigint) {
  if (bigint < 0) return NaN;

  const s = bigint.toString(16);
  const s15 = s.substring(0, 15);

  return Math.log(16) * (s.length - s15.length) + Math.log("0x" + s15);
}

const largeNumber = BigInt('9039845039485903949384755723427863486200719925474009384509283489374539477777093824750398247503894750384750238947502389475029384755555555555555555555555555555555555555554444444444444444444444444222222222222222222222255666666666666938475938475938475938408932475023847502384750923847502389475023987450238947509238475092384750923847502389457028394750293847509384570238497575938475938475938475938475555555555559843991');

console.log(natlog(largeNumber)); // 948.5641152531601
console.log(log10(largeNumber), log(largeNumber), log(-1))
// 411.95616098588766
// 948.5641152531603
// NaN

Logarithm-based solution calculates incorrect number of digits in large integers

The problem is that 99999999999999999 cannot be exactly represented as a (double precision) floating-point value in this case. The nearest value is 1.0E+17 when passed as a double parameter to log10.

The same would be true of the log10(n) value: 16.999999999999999995657... - the nearest value that can be represented is 17.

How to do a fractional power on BigDecimal in Java?

The solution for arguments under 1.7976931348623157E308 (Double.MAX_VALUE) but supporting results with MILLIONS of digits:

Since double supports numbers up to MAX_VALUE (for example, 100! in double looks like this: 9.332621544394415E157), there is no problem to use BigDecimal.doubleValue(). But you shouldn't just do Math.pow(double, double) because if the result is bigger than MAX_VALUE you will just get infinity. SO: use the formula X^(A+B)=X^A*X^B to separate the calculation to TWO powers, the big, using BigDecimal.pow, and the small (remainder of the 2nd argument), using Math.pow, then multiply. X will be copied to DOUBLE - make sure it's not bigger than MAX_VALUE, A will be INT (maximum 2147483647 but the BigDecimal.pow doesn't support integers more than a billion anyway), and B will be double, always less than 1. This way you can do the following (ignore my private constants etc):

    int signOf2 = n2.signum();
    try {
        // Perform X^(A+B)=X^A*X^B (B = remainder)
        double dn1 = n1.doubleValue();
        // Compare the same row of digits according to context
        if (!CalculatorUtils.isEqual(n1, dn1))
            throw new Exception(); // Cannot convert n1 to double
        n2 = n2.multiply(new BigDecimal(signOf2)); // n2 is now positive
        BigDecimal remainderOf2 = n2.remainder(BigDecimal.ONE);
        BigDecimal n2IntPart = n2.subtract(remainderOf2);
        // Calculate big part of the power using context -
        // bigger range and performance but lower accuracy
        BigDecimal intPow = n1.pow(n2IntPart.intValueExact(),
                CalculatorConstants.DEFAULT_CONTEXT);
        BigDecimal doublePow =
            new BigDecimal(Math.pow(dn1, remainderOf2.doubleValue()));
        result = intPow.multiply(doublePow);
    } catch (Exception e) {
        if (e instanceof CalculatorException)
            throw (CalculatorException) e;
        throw new CalculatorException(
            CalculatorConstants.Errors.UNSUPPORTED_NUMBER_ +
                "power!");
    }
    // Fix negative power
    if (signOf2 == -1)
        result = BigDecimal.ONE.divide(result, CalculatorConstants.BIG_SCALE,
                RoundingMode.HALF_UP);

Results examples:

50!^10! = 12.50911317862076252364259*10^233996181

50!^0.06 = 7395.788659356498101260513

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