Dynamic Programming Multi-Objective Combinatorial Optimization

This book introduces a fairly universal approach to the design and analysis of exact optimization algorithms for multi-objective combinatorial optimization problems. It proposes the circuits without repetitions representing the sets of feasible solutions along with the increasing and strictly increasing cost functions as a model for such problems. The book designs the algorithms for multi-stage and bi-criteria optimization and for counting the solutions in the framework of this model.

As applications, this book studies eleven known combinatorial optimization problems: matrix chain multiplication, global sequence alignment, optimal paths in directed graphs, binary search trees, convex polygon triangulation, line breaking (text justification), one-dimensional clustering, optimal bitonic tour, segmented least squares, optimization of matchings in trees, and 0/1 knapsack problem.

The results presented are useful for researchers in combinatorial optimization. This book is also useful as the basis for graduate courses.

Mankowski, M., & Moshkov, M. (2021). Dynamic Programming Multi-Objective Combinatorial Optimization. Studies in Systems, Decision and Control. doi:10.1007/978-3-030-63920-4

Springer Nature


Additional Links

Is New Version Of:

  • [Dissertation]
    Mankowski, M. (2020). Dynamic Programming Multi-Objective Combinatorial Optimization. KAUST Research Repository. DOI: 10.25781/KAUST-9FUC0 Handle: 10754/665627
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