Operations Research and Mathematical Programming
Operations research is the use of mathematical models, statistics and algorithms to help decisionmaking. Researchers use it most often to analyze complex realworld systems, typically with the goal of improving or optimizing performance.
Mathematical programming endeavors to find optimal solutions for a broad range of problems including medical, financial, scientific, and engineering problems.
Operations Research and Mathematical Programming at Rensselaer
At Rensselaer, conduct research on the development, evaluation, and comparison of serial and parallel algorithms for a variety of mathematical programming problems.
Researchers consider:
 Combining operations research and artificial intelligence problemsolving methods.
 Scalability of these methods to large problems in data mining.
 Mathematical programming approaches to other areas in computer science such as:
 Database query optimization.
 Stochastic programming.
Operations Research is also studied in the department of Decisions Sciences and Engineering Systems.
Current Projects
Research topics include:
 Interior point methods for linear, integer, and nonlinear programming.
 Branchandbound and branchandcut approaches to integer programming problems.
 Column generation methods.
 Financial optimization.
 Genetic algorithms.
 Tabu search.
 Mathematical programming approaches to problems in artificial intelligence such as:
 Machine learning.
 Neural networks.
 Support vector machines.
 Pattern recognition.
 Planning.
Faculty Researchers
Kristin Bennett
Joseph Ecker
John Mitchell
