combinatorial optimization

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

1 min read · Thu, Jan 16 2025

decision trees combinatorial optimization Combinatorial machine learning machine learning machine learning algorithm

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

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

Apr 13, 12:00 - 13:00

KAUST

data mining Dynamic programming combinatorial optimization

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

Dynamic Programming Multi-Objective Combinatorial Optimization

Michal A. Mankowski, Postdoctoral Research Fellow, Computer Science

Oct 13, 15:00 - 16:00

KAUST

combinatorial optimization global sequence alignment multi-stage optimization algorithm

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).

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

Oct 4, 12:00 - 13:00

B9 L2 R2322

Dynamic programming combinatorial optimization data mining

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read · Mon, Jul 9 2018

machine learning knowledge discovery data mining combinatorial optimization

A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.