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Mathematical Sciences
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Mathematical Sciences Research

Acoustics
Applied Geometry
Approximation Theory
Bioinformatics
Biomathematics
Chemically-Reacting Flows
Computational Logic
Dynamical Systems
Environmental Problems
Fluid Dynamics
Inverse Problems
Machine Learning
Math Education
Mathematical Physics
Multiphase Flows
Nonlinear Analysis
Nonlinear Materials
Nonlinear Waves
Operations Research and Mathematical Programming
Optimization
Perturbation Methods
Scientific Computing

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Mathematical Sciences Research
Operations Research and Mathematical Programming

Operations research is the use of mathematical models, statistics and algorithms to help decision-making. Researchers use it most often to analyze complex real-world 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 problem-solving 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.
  • Branch-and-bound and branch-and-cut 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

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