# Matrix chain multiplication

Matrix chain multiplication

Matrix chain multiplication is an optimization problem that can be solved using dynamic programming. Given a sequence of matrices, we want to find the most efficient way to multiply these matrices together. The problem is not actually to perform the multiplications, but merely to decide in which order to perform the multiplications.

We have many options because matrix multiplication is associative. In other words, no matter how we parenthesize the product, the result will be the same. For example, if we had four matrices A, B, C, and D, we would have:

(ABC)D = (AB)(CD) = A(BCD) = A(BC)D = ....

However, the order in which we parenthesize the product affects the number of simple arithmetic operations needed to compute the product, or the efficiency. For example, suppose A is a 10 × 30 matrix, B is a 30 × 5 matrix, and C is a 5 × 60 matrix. Then,

(AB)C = (10×30×5) + (10×5×60) = 1500 + 3000 = 4500 operations
A(BC) = (30×5×60) + (10×30×60) = 9000 + 18000 = 27000 operations.

Clearly the first method is the more efficient. Now that we have identified the problem, how do we determine the optimal parenthesization of a product of n matrices? We could go through each possible parenthesization (brute force), but this would require time exponential in the number of matrices, which is very slow and impractical for large n. The solution, as we will see, is to break up the problem into a set of related subproblems. By solving subproblems one time and reusing these solutions many times, we can drastically reduce the time required. This is known as dynamic programming.

## A Dynamic Programming Algorithm

To begin, let's assume that all we really want to know is the minimum cost, or minimum number of arithmetic operations, needed to multiply out the matrices. If we're only multiplying two matrices, there's only one way to multiply them, so the minimum cost is the cost of doing this. In general, we can find the minimum cost using the following recursive algorithm:

• Take the sequence of matrices and separate it into two subsequences.
• Find the minimum cost of multiplying out each subsequence.
• Add these costs together, and add in the cost of multiplying the two result matrices.
• Do this for each possible position at which the sequence of matrices can be split, and take the minimum over all of them.

For example, if we have four matrices ABCD, we compute the cost required to find each of (A)(BCD), (AB)(CD), and (ABC)(D), making recursive calls to find the minimum cost to compute ABC, AB, CD, and BCD. We then choose the best one. Better still, this yields not only the minimum cost, but also demonstrates the best way of doing the multiplication: just group it the way that yields the lowest total cost, and do the same for each factor.

Unfortunately, if we implement this algorithm we discover that it's just as slow as the naive way of trying all permutations! What went wrong? The answer is that we're doing a lot of redundant work. For example, above we made a recursive call to find the best cost for computing both ABC and AB. But finding the best cost for computing ABC also requires finding the best cost for AB. As the recursion grows deeper, more and more of this type of unnecessary repetition occurs.

One simple solution is called memoization: each time we compute the minimum cost needed to multiply out a specific subsequence, we save it. If we are ever asked to compute it again, we simply give the saved answer, and do not recompute it. Since there are about n2/2 different subsequences, where n is the number of matrices, the space required to do this is reasonable. It can be shown that this simple trick brings the runtime down to O(n3) from O(2n), which is more than efficient enough for real applications. This is top-down dynamic programming.

Pseudocode (without memoization):

```// Matrix Ai has dimension p[i-1] x p[i] for i = 1..n
Matrix-Chain-Order(int p[])
{
// length[p] = n + 1
n = p.length - 1;
// m[i,j] = Minimum number of scalar multiplications (i.e., cost)
// needed to compute the matrix A[i]A[i+1]...A[j] = A[i..j]
// cost is zero when multiplying one matrix
for (i = 1; i <= n; i++)
m[i,i] = 0;

for (L=2; L<=n; L++) { // L is chain length
for (i=1; i<=n-L+1; i++) {
j = i+L-1;
m[i,j] = MAXINT;
for (k=i; k<=j-1; k++) {
// q = cost/scalar multiplications
q = m[i,k] + m[k+1,j] + p[i-1]*p[k]*p[j];
if (q < m[i,j]) {
m[i,j] = q;
// s[i,j] = Second auxiliary table that stores k
// k      = Index that achieved optimal cost
s[i,j] = k;
}
}
}
}
}
```
• Note : The first index for p is 0 and the first index for m and s is 1

Another solution is to anticipate which costs we will need and precompute them. It works like this:

• For each k from 2 to n, the number of matrices:
• Compute the minimum costs of each subsequence of length k, using the costs already computed.

When we're done, we have the minimum cost for the full sequence. Although it also requires O(n3) time, this approach has the practical advantages that it requires no recursion, no testing if a value has already been computed, and we can save space by throwing away some of the subresults that are no longer needed. This is bottom-up dynamic programming: a second way by which this problem can be solved.

## An Even More Efficient Algorithm

An algorithm published in 1984 by Hu and Shing achieves nlog n complexity. They showed how the matrix chain multiplication problem can be transformed (or reduced) into the problem of partitioning a convex polygon into non-intersecting triangles.  This image illustrates possible triangulations of a hexagon:

Secondly, they developed an algorithm that finds an optimum solution for the partition problem in O(nlog n) time.

With n+1 matrices in the multiplication chain there are n binary operations and Cn ways of placing parenthesizes, where Cn is Catalan number.

## Generalizations

Although this algorithm applies well to the problem of matrix chain multiplication, researchers such as Gerald Baumgartner have noted that it generalizes well to solving a more abstract problem: given a linear sequence of objects, an associative binary operation on those objects, and a way to compute the cost of performing that operation on any two given objects (as well as all partial results), compute the minimum cost way to group the objects to apply the operation over the sequence.

One common special case of this is string concatenation. Say we have a list of strings. In C, for example, the cost of concatenating two strings of length m and n using strcat is O(m + n), since we need O(m) time to find the end of the first string and O(n) time to copy the second string onto the end of it. Using this cost function, we can write a dynamic programming algorithm to find the fastest way to concatenate a sequence of strings (although this is rather useless, since we can concatenate them all in time proportional to the sum of their lengths). A similar problem exists for singly linked lists.

Another generalization is to solve the problem when many parallel processors are available. In this case, instead of adding the costs of computing each subsequence, we just take the maximum, because we can do them both simultaneously. This can drastically affect both the minimum cost and the final optimal grouping; more "balanced" groupings that keep all the processors busy are favored. Heejo Lee et al. describe even more sophisticated approaches.

## Implementations

Wikimedia Foundation. 2010.

### См. также в других словарях:

• Matrix - получить на Академике рабочий купон на скидку Строительный Двор или выгодно matrix купить с бесплатной доставкой на распродаже в Строительный Двор

• Matrix multiplication — In mathematics, matrix multiplication is a binary operation that takes a pair of matrices, and produces another matrix. If A is an n by m matrix and B is an m by p matrix, the result AB of their multiplication is an n by p matrix defined only if… …   Wikipedia

• Matrix (mathematics) — Specific elements of a matrix are often denoted by a variable with two subscripts. For instance, a2,1 represents the element at the second row and first column of a matrix A. In mathematics, a matrix (plural matrices, or less commonly matrixes)… …   Wikipedia

• Matrix calculus — Topics in Calculus Fundamental theorem Limits of functions Continuity Mean value theorem Differential calculus  Derivative Change of variables Implicit differentiation Taylor s theorem Related rates …   Wikipedia

• List of mathematics articles (M) — NOTOC M M estimator M group M matrix M separation M set M. C. Escher s legacy M. Riesz extension theorem M/M/1 model Maass wave form Mac Lane s planarity criterion Macaulay brackets Macbeath surface MacCormack method Macdonald polynomial Machin… …   Wikipedia

• List of terms relating to algorithms and data structures — The [http://www.nist.gov/dads/ NIST Dictionary of Algorithms and Data Structures] is a reference work maintained by the U.S. National Institute of Standards and Technology. It defines a large number of terms relating to algorithms and data… …   Wikipedia

• Список терминов, относящихся к алгоритмам и структурам данных —   Это служебный список статей, созданный для координации работ по развитию темы.   Данное предупреждение не устанавливается на информационные списки и глоссарии …   Википедия

• Список терминов — Список терминов, относящихся к алгоритмам и структурам данных   Это сл …   Википедия

• Category:Optimization algorithms — An optimization algorithm is an algorithm for finding a value x such that f(x) is as small (or as large) as possible, for a given function f, possibly with some constraints on x. Here, x can be a scalar or vector of continuous or discrete values …   Wikipedia

• Time complexity — In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the size of the input to the problem. The time complexity of an algorithm is commonly expressed using big O… …   Wikipedia

• Constraint algorithm — In mechanics, a constraint algorithm is a method for satisfying constraints for bodies that obey Newton s equations of motion. There are three basic approaches to satisfying such constraints: choosing novel unconstrained coordinates ( internal… …   Wikipedia

### Поделиться ссылкой на выделенное

##### Прямая ссылка:
Нажмите правой клавишей мыши и выберите «Копировать ссылку»