combinatorial optimization

Learning under Limited Information across Federated, Multi-Agent, and LLM Settings

Salma Kharrat, Ph.D. Student, Computer Science

May 7, 15:00 - 16:45

B3 R5220

Federated learning personalized learning decentralized learning Reinforcement Learning black-box optimization prompt optimization decentralized systems combinatorial optimization observability inference Trustworthy AI trustworthy machine learning intelligent systems LLM

This dissertation studies learning under structural information constraints across three major paradigms: federated learning, cooperative multi-agent reinforcement learning, and black-box optimization of large language models.

KAUST researchers publish new book on decision rule systems

1 min read · Thu, Apr 23 2026

decision trees computation combinatorial optimization computational geometry

KAUST Postdoctoral Research Fellows Kerven Durdymyradov and Azimkhon Ostonov, along with Professor Mikhail Moshkov, have published a new book with Springer titled "Transforming Decision Rule Systems into Decision Trees: Syntactic Approach." The book is devoted to the transformation of decision rule systems into deterministic and nondeterministic decision trees that recognize the properties of these systems. It continues the development of the syntactic approach to the study of the transformation problem, which assumes the input data is unknown and only a system of decision rules exists to be

Professor Mikhail Moshkov’s new book published by Springer

1 min read · Tue, Aug 26 2025

decision trees computation combinatorial optimization computational geometry

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

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 Science

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, Ph.D., 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 Science

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.

Salma Kharrat

Ph.D. Student, Computer Science

Federated learning Trustworthy AI trustworthy machine learning intelligent systems decentralized systems combinatorial optimization personalized learning decentralized learning Reinforcement Learning observability inference black-box optimization LLM prompt optimization