Multiplication algorithm

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A multiplication algorithm is an algorithm (or method) to multiply two numbers. Depending on the size of the numbers, different algorithms are in use. Efficient multiplication algorithms have been around since the advent of the decimal system.

Contents

1 Long multiplication 1.1 Example 2 Shifts and adds 3 Multiplication algorithms for computer algebra 3.1 Karatsuba multiplication 3.2 Toom-Cook 3.3 Fourier transform methods 4 Polynomial multiplication 5 See also 6 External links

Long multiplication

A major advantage of positional numeral systems over other systems of writing down numbers is that they facilitate the usual grade-school method of long multiplication: multiply the first number with every digit of the second number and then add up all the properly shifted results. In order to perform this algorithm, one needs to know the products of all possible digits, which is why multiplication tables have to be memorized. Humans use this algorithm in base 10, while computers employ the same algorithm in base 2. The algorithm is a lot simpler in base 2, since the multiplication table has only 4 entries. Rather than first computing the products, and then adding them all together in a second phase, computers add the products to the result as soon as they are computed. Modern chips implement this algorithm for 32-bit or 64-bit numbers in hardware or in microcode. To multiply two numbers with n digits using this method, one needs about n² operations. More formally: the time complexity of multiplying two n-digit numbers using long multiplication is Ο(n²).

Example

           23958233
               5830
           _________
           00000000       
          71874699
        191665864
       119791165
      -------------
       139676498390

Shifts and adds

An old method for multiplication, that does not require multiplication tables, is the Peasant multiplication algorithm; this is actually a method of multiplication using base 2.

A similar technique is still in use in computers where a binary number is multiplied by a small integer constant. Since multiplication of a binary number by powers of two can expressed in terms of bit-shifts, a series of bit shifts and addition operations which has the effect of performing a multiplication without the use of any conditional logic or hardware multiplier. For many processors, this is often the fastest way to perform these simple multiplications.

Multiplication algorithms for computer algebra

Karatsuba multiplication

For systems that need to multiply numbers in the range of several thousand digits, such as computer algebra systems and bignum libraries, long multiplication is too slow. These systems employ Karatsuba multiplication which was discovered in 1962 and proceeds as follows. To multiply two n-digit numbers x and y represented in base W, where n is even and equal to 2m (if n is odd instead, or the numbers are not of the same length, this can be corrected by adding zeros at the left end of x and/or y), we can write

x = x₁ W^m + x₂

y = y₁ W^m + y₂

with m-digit numbers x₁, x₂, y₁ and y₂. The product is given by

xy = x₁y₁ W^2m + (x₁y₂ + x₂y₁) W^m + x₂y₂

so we need to quickly determine the numbers x₁y₁, x₁y₂ + x₂y₁ and x₂y₂. The heart of Karatsuba's method lies in the observation that this can be done with only three rather than four multiplications:

1. compute x₁y₁, call the result A
2. compute x₂y₂, call the result B
3. compute (x₁ + x₂)(y₁ + y₂), call the result C
4. compute C - A - B; this number is equal to x₁y₂ + x₂y₁.

To compute these three products of m-digit numbers, we can employ the same trick again, effectively using recursion. Once the numbers are computed, we need to add them together, which takes about n operations.

If T(n) denotes the time it takes to multiply two n-digit numbers with Karatsuba's method, then we can write

T(n) = 3 T(n/2) + cn + d

for some constants c and d, and this recurrence relation can be solved, giving a time complexity of Θ(n^ln(3)/ln(2)). The number ln(3)/ln(2) is approximately 1.585, so this method is significantly faster than long multiplication. Because of the overhead of recursion, Karatsuba's multiplication is not very fast for small values of n; typical implementations therefore switch to long multiplication if n is below some threshold.

Toom-Cook

Another method of multiplication is called Toom-Cook or Toom3. The Toom-Cook method splits each number to be multiplied into multiple parts. Karatsuba is a special case of Toom-Cook using two parts. A three-way Toom-Cook can do a size-N³ multiplication for the cost of five size-N multiplications, improvement by a factor of 9/5 compared to the Karatsuba method's improvement by a factor of 4/3. Using more parts eventually comes up against the law of diminishing returns.

Fourier transform methods

There exist even faster algorithms, based on the fast Fourier transform. The idea, due to Strassen (1968), is the following: multiplying two numbers represented as digit strings is virtually the same as computing the convolution of those two digit strings. Instead of computing a convolution, one can instead first compute the discrete Fourier transforms, multiply them entry by entry, and then compute the inverse Fourier transform of the result. (See convolution theorem.) The fastest known method based on this idea was described in 1971 by Schönhage and Strassen (Schönhage-Strassen algorithm) and has a time complexity of Θ(n ln(n) ln(ln(n))).

The GIMPS distributed Internet prime search project deals with numbers having several million digits and also employs a Fourier transform based multiplication algorithm.

Using number-theoretic transforms instead of discrete Fourier transforms should avoid any rounding error problems by using modular arithmetic instead of complex numbers.

Polynomial multiplication

All the above multiplication algorithms can also be expanded to multiply polynomials.

See also

• Strassen algorithm

External links

• Multiplication Algorithms used by GMP (http://www.swox.com/gmp/manual/Multiplication-Algorithms.html#Multiplication%20Algorithms)