About

Professor Sergiy Butenko is a faculty member in the Department of Industrial & Systems Engineering at Texas A&M University. He holds the position of Liollio Family Faculty Fellow and can be contacted via phone at 979-458-2333 or email at butenko@tamu.edu. His educational background includes a Ph.D. in Industrial & Systems Engineering from the University of Florida, obtained in 2003. His research concentrates mainly on global and discrete optimization and their applications. Specifically, he is interested in the theoretical and computational aspects of continuous global optimization approaches for solving discrete optimization problems on graphs. His work involves applications such as network-based data mining, analysis of biological and social networks, wireless ad hoc and sensor networks, and energy.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Data Mining
• Computer Security
• Engineering
• Economics
• Algorithm
• Combinatorics
• Operations management
• Risk analysis (engineering)
• Mathematical optimization
• Reliability engineering
• Microeconomics
• Industrial engineering
• Manufacturing engineering
• Business
• Operations research

Selected publications

• Regularized standard polynomial programming formulations for the maximum clique problem

  Computational Optimization and Applications · 2025-03-10 · 2 citations

  articleCorresponding
  PublisherDOI

• An Implicit Enumeration Approach for Maximum Ratio Clique Relaxations

  Networks · 2025-06-13

  articleOpen access

  ABSTRACT This article proposes an implicit enumeration approach to solve the maximum ratio ‐plex and the maximum ratio ‐defective clique problems. The approach is inspired by the classical Bron‐Kerbosch algorithm for enumerating all maximal cliques in a graph, which is extended to enumerating structures that are hereditary on induced subgraphs. Such structures include ‐plexes and ‐defective cliques, among many others. The performance of the proposed approach is compared with that of the methods based on mixed integer linear programming (MILP), binary search, and Newton's iteration through numerical experiments on randomly generated and real‐life network instances.

  PublisherDOI

• The Maximum Clique and Vertex Coloring

  2025-01-01

  book-chapterSenior author
  PublisherDOI

• The Maximum Clique and Vertex Coloring

  2025-01-01

  book-chapterSenior author
  PublisherDOI

• On Interdicting Dense Clusters in a Network

  INFORMS journal on computing · 2024-10-29

  articleOpen access

  Given a vertex-weighted undirected graph with blocking costs of its vertices and edges, we seek a minimum cost subset of vertices and edges to block such that the weight of any γ-quasi-clique in the interdicted graph is at most some predefined threshold parameter. The value of [Formula: see text] specifies the edge density of cohesive vertex groups of interest in the network. The considered weighted γ-quasi-clique interdiction problem can be viewed as a natural generalization of several variations of the clique blocker problem previously studied in the literature. From the application perspective, this setting is primarily motivated by the problem of disrupting adversarial (“dark”) networks (e.g., social or communication networks), where γ-quasi-cliques represent “tightly knit” groups of adversaries that we aim to dismantle. We first address the theoretical computational complexity of the problem. We then exploit some basic characterization of its feasible solutions to derive a linear integer programming (IP) formulation. This linear IP model can be solved using a lazy-fashioned branch-and-cut scheme. We also propose a combinatorial branch-and-bound algorithm for solving this problem. The computational performance of the developed exact solution schemes is studied using a test bed of randomly generated and real-life networks. Finally, some interesting insights and observations are also provided using a well-known example of a terrorist network. History: Accepted by Russel Bent, Area Editor for Network Optimization: Algorithms & Applications. Funding: The work of S. Butenko was partially supported by the Air Force Office of Scientific Research under Award FA9550-23-1-0300. The work of O. A. Prokopyev was partially supported by the Office of Naval Research under Award ONR N00014-22-1-2678. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0027 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0027 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ . The online appendix is available at https://doi.org/10.1287/ijoc.2023.0027 .

  PublisherOA PDFDOI

• Code and Data Repository for On Interdicting Dense Clusters in a Network

  INFORMS journal on computing · 2024-10-29

  article

  Source codes and data set used in “On interdicting dense clusters in a network” paper. Data set contains 20 randomly-generated networks and 38 real-life networks. Each file contains 1+X+Y lines, where X and Y are the number of vertices and the number of edges in the network, respectively. It starts with two numbers indicating the number of vertices X and the number of edges Y. Then, it follows with X lines of two numbers. Note that we do not assign indices to the vertices. The index of a vertex is the same as the line number it appears on. The first number in each line is the weight of a vertex and the second number is its blocking cost. The last Y lines contain three numbers. The first and second numbers in each line are the head and the tail of an edge. Note that the edges are undirected. The third number is the blocking cost of an edge.

  PublisherDOI

• A PageRank-Based Method for College Football Recruiting Rankings

  Springer optimization and its applications · 2024-10-18

  book-chapter1st authorCorresponding
  PublisherDOI

• A Network-Based Risk-Averse Approach to Optimizing the Security of a Nuclear Facility

  Springer optimization and its applications · 2023-01-01

  book-chapter
  PublisherDOI

• Dynamic risk analysis of evolving scenarios in oil and gas separator

  Reliability Engineering & System Safety · 2023 · 40 citations

  • Computer Science
  • Data Mining
  • Computer Science
  PublisherDOI

• Can Fast Food Eating Patterns Be Healthy? An Optimization-Based Analysis of Popular US Fast-Food Chains

  Operations Research Forum · 2023-03-08

  article
  PublisherDOI

Recent grants

• U.S.-Ukraine International Research Experience for Students: Discrete and Nondifferentiable Optimization: Theory, Algorithms, and Applications

  NSF · $123k · 2006–2009

• Continuous Approaches to Optimization Problems in Graphs

  NSF · $219k · 2015–2018

• IRES: International Experience for Students: U.S.-Ukraine Collaboration on Discrete and Nondifferentiable Optimization

  NSF · $82k · 2009–2012

Frequent coauthors

• Pãnos M. Pardalos

  22 shared

• Vladimir Boginski

  University of Central Florida

  19 shared

• Pãnos M. Pardalos

  University of Florida

  19 shared

• Seyedmohammadhossein Hosseinian

  11 shared

• Oleg A. Prokopyev

  10 shared

• Balabhaskar Balasundaram

  Oklahoma State University Oklahoma City

  9 shared

• Christodoulos A. Floudas

  9 shared

• Themistocles M. Rassias

  National Technical University of Athens

  9 shared

Awards & honors

• Charles H. Barclay, Jr. Faculty Fellow, Texas A&M University…
• Young Investigator Program Award, Air Force Office of Scient…
• Favorite Professor of the Year, IIE Student Chapter, Texas A…
• Outstanding Teaching Assistant Award, ISE, University of Flo…
• Graduate Student Award for Excellence in Research, ISE, Univ…