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-convex Optimization Problem With and Without Constraints -first Order Optimization Methods -first Order Stochastic Optimization Methods and The Sgd Algorithm -momentum Methods and Nesterov S Accelerated Methods -the Frank-wolfe Method -duality -kkt Conditions -newton S Method. Learning Outcomes# After The Successful Completion of The Course# 1. The Students Will Be Familiar With Definitions and Properties Of Optimization Problems, and Will Know to Read and Write Such Problems By Themselves._ 2. The Students Will Be Familiar With Popular Optimization Methods, Their Advantages and Their Disadvantages.

Faculty: Electrical and Computer Engineering
|Undergraduate Studies |Graduate Studies

Pre-required courses

(104013 - Differential and Integral Calculus 2t and 104016 - Algebra 1/extended) or 104034 - Introduction to Probability H

Course with no extra credit

104193 - Optimization Theory 236330 - Introduction to Optimization

• Convex optimization - Boyd, Stephen P.