combinatorial optimization

Professor Mikhail Moshkov’s new book published by Springer

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Professor Mikhail Moshkov’s new book, “Computation Trees: A Generalization of Decision Trees,” has been published by Springer. Moshkov’s book is devoted to the study of deterministic and nondeterministic computation trees. Computation trees are a natural generalization of decision trees: in addition to the one-place predicate-type operations (attributes) used in decision trees, computation trees can use multi-place predicate and function operations. These models arise in areas such as combinatorial optimization, computational geometry, and classification or prediction tasks, particularly when
Decision Trees Versus Systems of Decision Rules

New book by KAUST Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov and Professor Mikhail Moshkov published by Springer

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New book by Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov explores decision trees and rule systems using rough set theory.

Dynamic Programming Multi-Objective Combinatorial Optimization

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KAUST

In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multi-objective optimization algorithms: the multi-stage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bi-criteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization).