Formulate linear and integer programming problems for solving commonly encountered optimization problems. Understand how approximation algorithms compute solutions that are guaranteed to be within ...

Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering.

Multiplayer algorithms (particle swarm, leapfrogging, differential evolution) broadly explore the surface, increasing the probability that the global will be discovered. Interior/exterior point: ...

Course Description This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; ...

We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of ...

One course in optimization (including primal, dual problems, Lagrangian functions), linear algebra, calculus, familiar with real analysis or permission of instructor. Large-scale networks and datasets ...

Projection algorithms are well known for their simplicity and flexibility in solving feasibility problems. They are particularly important in practice owing to minimal requirements for software ...

Thanks to these abilities, they can already topple complex linear differential equations exponentially faster than classical machines. Researchers have long hoped they could similarly tame nonlinear ...