An Efficient Algorithm for Reducing the Duality Gap in a Special Class of the Knapsack Problem
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Abstract
A special class of the knapsack problem is called the separable nonlinear knapsack problem. This problem has received considerable attention recently because of its numerous applications. Dynamic programming is one of the basic approaches for solving this problem. Unfortunately, the size of state-pace will dramatically increase and cause the dimensionality problem. In this paper, an efficient algorithm is developed to find surrogate multipliers in each stage of dynamic
programming in order to transform the original problem to a single constraint problem called surrogate problem. The upper and lower bounds obtained by solving the surrogate problem can eliminate a large number of state variables in dynamic programming and extremely reduce the duality gap according to our computational results.
programming in order to transform the original problem to a single constraint problem called surrogate problem. The upper and lower bounds obtained by solving the surrogate problem can eliminate a large number of state variables in dynamic programming and extremely reduce the duality gap according to our computational results.
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