Knapsack Solutions Research Articles

Private Information

This article studies the notion of quantitative policies for trust management and gives protocols for realizing them in a disclosure-minimizing fashion. Specifically, Bob values each credential with a certain number of points, and requires a minimum total threshold of points before granting Alice access to a resource. In turn, Alice values each of her credentials with a privacy score that indicates her degree of reluctance to reveal that credential. Bob's valuation of credentials and his threshold are private. Alice's privacy-valuation of her credentials is also private. Alice wants to find a subset of her credentials that achieves Bob's required threshold for access, yet is of as small a value to her as possible. We give protocols for computing such a subset of Alice's credentials without revealing any of the two parties' above-mentioned private information. Furthermore, we develop a fingerprint method that allows Alice to independently and easily recover the optimal knapsack solution, once the computed optimal value is given, but also enables verification of the integrity of the optimal value. The fingerprint method is useful beyond the specific authorization problem studied, and can be applied to any integer knapsack dynamic programming in a private setting.

Exploiting nested inequalities and surrogate constraints

The exploitation of nested inequalities and surrogate constraints as originally proposed in Glover [Glover, F., 1965. A multiphase-dual algorithm for the zero–one integer programming problem. Operations Research 13, 879–919; Glover, F., 1971. Flows in arborescences. Management Science 17, 568–586] has been specialized to multidimensional knapsack problems in Osorio et al. [Osorio, M.A., Glover, F., Hammer, P., 2002. Cutting and surrogate constraint analysis for improved multidimensional knapsack solutions. Annals of Operations Research 117, 71–93]. We show how this specialized exploitation can be strengthened to give better results. This outcome results by a series of observations based on surrogate constraint duality and properties of nested inequalities. The consequences of these observations are illustrated by numerical examples to provide insights into uses of surrogate constraints and nested inequalities that can be useful in a variety of problem settings.

Random Walks on Truncated Cubes and Sampling 0-1 Knapsack Solutions

We solve an open problem concerning the mixing time of symmetric random walk on the n-dimensional cube truncated by a hyperplane, showing that it is polynomial in n. As a consequence, we obtain a fully polynomial randomized approximation scheme for counting the feasible solutions of a 0-1 knapsack problem. The results extend to the case of any fixed number of hyperplanes. The key ingredient in our analysis is a combinatorial construction we call a "balanced almost uniform permutation," which is of independent interest.

Cutting and Surrogate Constraint Analysis for Improved Multidimensional Knapsack Solutions

We use surrogate analysis and constraint pairing in multidimensional knapsack problems to fix some variables to zero and to separate the rest into two groups – those that tend to be zero and those that tend to be one, in an optimal integer solution. Using an initial feasible integer solution, we generate logic cuts based on our analysis before solving the problem with branch and bound. Computational testing, including the set of problems in the OR-library and our own set of difficult problems, shows our approach helps to solve difficult problems in a reasonable amount of time and, in most cases, with a fewer number of nodes in the search tree than leading commercial software.

A New Knapsack Solution Approach by Integer Equivalent Aggregation and Consistency Determination

We present a new and highly efficient algorithm for the integer knapsack problem based on a special strategy for aggregating integer-valued equations. Employing a new theorem for creating a single equation with the same nonnegative integer solution set as a system of original equations, we transform the integer knapsack problem into an equivalent problem of determining the consistency of an aggregated equation for a parameterized right hand side. This last problem is solved by a newly developed algorithm with complexity O(min(n α1, n + α12)), where n is the number of variables and α1 is the smallest coefficient in the aggregated equation. Empirical outcomes show our procedure is significantly superior to advanced branch-and-bound methods (previously established to be the most efficient knapsack solution procedures), obtaining solutions several orders of magnitude faster for hard problems.

New results for aggregating integer-valued equations

A variety of results have been given for aggregating integer-valued (diophantine) equations whose variables are restricted to nonnegative integers. In each, integer weights are identified for the equations so that their linear combination yields a single equation with the same solution set of the original system of equations. Because the coefficients of the aggregated equation tend to achieve unwieldy sizes as the number of original equations increases, the goal is to identify weights so these coefficients will lie in a range as limited as possible. We give theorems which separately and in combination provide new methods for aggregating general integer-valued equations. Our results include formulations that do not require linearity of the original system, or nonnegativity of component variables. We also demonstrate that our theorems yield as special cases earlier results (analytical formulae) conjectured to yield the smallest possible weights for less general domains. As another application, the presented results were used to develop a highly efficient approach for the integer knapsack problem. Empirical outcomes show that the developed solution procedure is significantly superior to advanced branch and bound methods (previously established to be the most efficient knapsack solution procedures).