Approximate algorithms for the knapsack problem on parallel computers

Computing an optimal solution to the knapsack problem is known to be NP-hard. Consequently, fast parallel algorithms for finding such a solution without using an exponential number of processors appear unlikely. An attractive alternative is to compute an approximate solution to this problem rapidly using a polynomial number of processors. In this paper, we present an efficient parallel algorithm for finding approximate solutions to the 0-1 knapsack problem. Our algorithm takes an ε, 0 < ε < 1, as a parameter and computes a solution such that the ratio of its deviation from the optimal solution is at most a fraction ε of the optimal solution. For a problem instance having n items, this computation uses O(n 5 2/ε 3 2) processors and requires O(log3n + log2nlog( 1 ε)) time. The upper bound on the processor requirement of our algorithm is established by reducing it to a problem on weighted bipartite graphs. This processor complexity is a significant improvement over that of other known parallel algorithms for this problem. © 1991.