Optimization is a core mathematical tool for selecting a best solution among a possibly infinite number of alternatives. As a tool, it appears in most facets of industrial engineering, decision sciences, algorithm design, machine learning, and the management sciences including applications in healthcare, logistics, manufacturing, data science, and sports analytics. Theoretic and computational advancements in the area can drastically improve our ability to quickly make fast, effective decisions in a countless array of applications.
Image Source: https://easyai.tech/en/ai-definition/gradient-descent/
Active Research Areas at RPI Include:
Online optimization techniques for multiagent systems
Conic optimization and generation of cutting planes
Affiliated Faculty
Recent Papers by Affiliated Faculty
Lattice Closures of Polyhedra (Dr. Moran)
Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent (Dr. Bailey)
Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System (Dr. Bailey)
Optimizing Edge Sets in Networks to Produce Ground Truth Communities Based on Modularity (Dr. Mitchell)
Affiliated PhD Students:
Related Courses at RPI:
ISYE 4500: Stochastic Methods in Operations Research
ISYE 4600: Deterministic Methods in Operations Research
ISYE 6550/CS 6966: Network Flows: Theory, Algorithms, and Applications
ISYE 6610: Systems Modeling in DSES
ISYE 6760/MATP 6620 Integer and Combinatorial Optimization
ISYE 6770/MATP 6640 Linear and Conic Optimization
ISYE 6780/MATP 6600 Introduction to Optimization
