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gives an introduction to optimization and includes formulating optimization problems as mathematical models and solution techniques using computers.

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contains solution methods for linear optimization problems, and some methods for solving integer and non-linear optimization problems. It covers the simplex method and the interior point methods for linear programming, network algorithms, branch and bound method for integer optimization problems, duality theory, and sensitivity analysis.

Click here →ÌýINF 270

covers theory and algorithms for solving integer and combinatorial optimization problems, including network flow problems, matching, assignment and knapsack problems, dynamic programming, tree search methods, cutting plane methods and polyhedral theory.

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covers the basic framework for constructing efficient methods for solving unconstrained optimization problems. Topics include line search, trust region and derivative-free methods for unconstrained optimization. For constrained optimization the Karush-Kuhn-Tucker theory and basic solution techniques are presented. The close connection to Machine Learning and stochastic gradient descent is discussed.ÌýÌý

Click here →ÌýINF 272

The course explores the metaheuristic optimization algorithms. Topics that are covered include heuristics and approximation algorithms, local search, simulated annealing, tabu search, genetic algorithms, ant-colony, particle swarm, variable neighborhood search, adaptive large neighborhood search, hybrid algorithms and mathheuristics. The course contains a wide range of practical optimization problems as case studies.

Click here →ÌýINF 273

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