The properties of matrix addition and multiplication depend on the properties of the underlying number system. In this section, we define three different kinds of underlying number systems: quasirings, rings, and fields. We can define matrix multiplication over quasirings, and Strassen s matrix-multiplication algorithm works over rings. We then present a simple trick for reducing boolean matrix multiplication, which is defined over a quasiring that is not a ring, to multiplication over a ring. Finally, we discuss why the properties of a field cannot naturally be exploited to provide better algorithms for matrix multiplication.
Let denote a number system, where S is a set of elements, and are binary operations on S (the addition and multiplication operations, respectively), and are distinct distinguished elements of S. This system is a quasiring if it satisfies the following properties:
1. is a monoid:
is an identity for ; that is, for all a S.
Likewise, is a monoid.
2. is an annihilator, that is, for all a S.
3. The operator is commutative; that is, a b = b a for all a, b S.
Examples of quasirings include the boolean quasiring (, , 0, 1), where denotes logical OR and denotes logical AND, and the natural number system (N, +, , 0, 1), where + and denote ordinary addition and multiplication. Any closed semiring (see Section 26.4) is also a quasiring; closed semirings obey additional idempotence and infinite-sum properties.
We can extend and to matrices as we did for + and in Section 1. Denoting the n n identity matrix composed of we find that matrix multiplication is well defined and the matrix system is itself a quasiring, as the following theorem states.
If is a quasiring and n 1, then is a quasiring.
Proof The proof is left as Exercise 3-3.
Subtraction is not defined for quasirings, but it is for a ring, which is a quasiring that satisfies the following additional property:
5. Every element in S has an additive inverse; that is, for all a S, there exists an element b S such that . Such a b is also called the negative of a and is denoted (-a).
Given that the negative of any element is defined, we can define subtraction by a - b = a + (-b).
There are many examples of rings. The integers (Z, +, , 0, 1) under the usual operations of addition and multiplication form a ring. The integers modulo n for any integer n > 1 that is, (Zn, +, , 0, 1), where + is addition modulo n and is multiplication modulo n--form a ring. Another example is the set R[x] of finite-degree polynomials in x with real coefficients under the usual operations--that is, (R[x], +, , 0, 1), where + is polynomial addition and is polynomial multiplication.
The following corollary shows that Theorem 7 generalizes naturally to rings.
If is a ring and n 1, then is a ring.
Proof The proof is left as Exercise 3-3.
Using this corollary, we can prove the following theorem.
Strassen's matrix-multiplication algorithm works properly over any ring of matrix elements.
Proof Strassen's algorithm depends on the correctness of the algorithm for 2 2 matrices, which requires only that the matrix elements belong to a ring. Since the matrix elements do belong to a ring, Corollary 8 implies the matrices themselves form a ring. Thus, by induction, Strassen's algorithm works correctly at each level of recursion.
Strassen's algorithm for matrix multiplication, in fact, depends critically on the existence of additive inverses. Out of the seven products P1, P2, . . . ,P7, four involve differences of submatrices. Thus, Strassen's algorithm does not work in general for quasirings.
Strassen's algorithm cannot be used directly to multiply boolean matrices, since the boolean quasiring (, , 0, 1) is not a ring. There are instances in which a quasiring is contained in a larger system that is a ring. For example, the natural numbers (a quasiring) are a subset of the integers (a ring), and Strassen's algorithm can therefore be used to multiply matrices of natural numbers if we consider the underlying number system to be the integers. Unfortunately, the boolean quasiring cannot be extended in a similar way to a ring. (See Exercise 3-4.)
The following theorem presents a simple trick for reducing boolean matrix multiplication to multiplication over a ring. Problem 31-1 presents another efficient approach.
If M(n) denotes the number of arithmetic operations required to multiply two n n matrices over the integers, then two n n boolean matrices can be multiplied using O(M(n)) arithmetic operations.
Proof Let the two matrices be A and B, and let C = AB in the boolean quasiring, that is,
Instead of computing over the boolean quasiring, we compute the product C' over the ring of integers with the given matrix-multiplication algorithm that uses M(n) arithmetic operations. We thus have
Each term aikbkj of this sum is 0 if and only if aik bkj = 0, and 1 if and only if aik bkj = 1. Thus, the integer sum is 0 if and only if every term is 0 or, equivalently, if and only if the boolean OR of the terms, which is cij, is 0. Therefore, the boolean matrix C can be reconstructed with (n2) arithmetic operations from the integer matrix C' by simply comparing each with 0. The number of arithmetic operations for the entire procedure is then O(M(n)) +(n2) = O(M(n)), since M(n) = (n2).
Thus, using Strassen's algorithm, we can perform boolean matrix multiplication in O(n lg 7) time.
The normal method of multiplying boolean matrices uses only boolean variables. If we use this adaptation of Strassen's algorithm, however, the final product matrix can have entries as large as n, thus requiring a computer word to store them rather than a single bit. More worrisome is that the intermediate results, which are integers, may grow even larger. One method for keeping intermediate results from growing too large is to perform all computations modulo n + 1. Exercise 3-5 asks you to show that working modulo n + 1 does not affect the correctness of the algorithm.
A ring is a field if it satisfies the following two additional properties:
6. The operator is commutative; that is, for all a, b S.
7. Every nonzero element in S has a multiplicative inverse; that is, for all , there exists an element b S such that .
Such an element b is often called the inverse of a and is denoted a-1.
Examples of fields include the real numbers (R, +, , 0, 1), the complex numbers (C, +, , 0, 1), and the integers modulo a prime p: (Zp, +, , 0, 1).
Because fields offer multiplicative inverses of elements, division is possible. They also offer commutativity. By generalizing from quasirings to rings, Strassen was able to improve the running time of matrix multiplication. Since the underlying elements of matrices are often from a field--the real numbers, for instance--one might hope that by using fields instead of rings in a Strassen-like recursive algorithm, the running time might be further improved.
This approach seems unlikely to be fruitful. For a recursive divide-and-conquer algorithm based on fields to work, the matrices at each step of the recursion must form a field. Unfortunately, the natural extension of Theorem 7 and Corollary 8 to fields fails badly. For n > 1, the set of n n matrices never forms a field, even if the underlying number system is a field. Multiplication of n n matrices is not commutative, and many n n matrices do not have inverses. Better algorithms for matrix multiplication are therefore more likely to be based on ring theory than on field theory.
Does Strassen's algorithm work over the number system (Z[x], +, , 0, 1), where Z[x] is the set of all polynomials with integer coefficients in the variable x and + and are ordinary polynomial addition and multiplication?
Explain why Strassen's algorithm doesn't work over closed semirings (see Section 26.4) or over the boolean quasiring (,
Prove Theorem 7 and Corollary 8.
Show that the boolean quasiring (, , 0, 1) cannot be embedded in a ring. That is, show that it is impossible to add a '-1' to the quasiring so that the resulting algebraic structure is a ring.
Argue that if all computations in the algorithm of Theorem 10 are performed modulo n + 1, the algorithm still works correctly.
Show how to determine efficiently if a given undirected input graph contains a triangle (a set of three mutually adjacent vertices).
Show that computing the product of two n n boolean matrices over the boolean quasiring is reducible to computing the transitive closure of a given directed 3n-vertex input graph.
Show how to compute the transitive closure of a given directed n-vertex input graph in time O(nlg 7 lg n). Compare this result with the performance of the TRANSITIVE CLOSURE procedure in Section 26.2.
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