Introduction to algorithms
Efficient Algorithms for the Hitchcock Transportation Problem
SIAM Journal on Computing
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
A Matching Scheme to Enhance Performance Evaluation of Raster-to-Vector Conversion Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A formal analysis of why heuristic functions work
Artificial Intelligence
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We present a combinatorial optimization problem with useful applications in the fields of task assignment, memory allocation, and image document analysis. Our motivation is to find a mapping that optimizes an objective function defined as a sum of averaged elementary gains. The problem is conjectured to be NP-hard, and solution we propose is heuristic-based. It utilizes a greedy heuristic function which combines optimal solutions of small-sized sub-problems to yield a potential solution to the original problem. The solutions of the sub-problem are, in turn, related to what we call saddle points of the underlying sub-problem. The proposed algorithm has been extensively tested. In the case of problems with a small number of elements, the reported solution has been compared to the optimal solution determined by exhaustively searching the solution space, and in these cases, the heuristic solution obtained is remarkably close to the optimal one. For problems of larger magnitude, the computed heuristic solution is compared to the value obtained by a greedy solution, and our algorithm is markedly superior in almost every case.