Convex optimization berkeley

Catalog Description: Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex.

Is a convex problem easy?

In the early days of optimization, it was thought that linearity was what distinguished a hard problem from an easy one

Today, it appears that convexity is the relevant notion

Roughly speaking, a convex problem is easy

In this course, we will re ne this statement

What is convex optimization?

Catalog Description: Convex optimization as a systematic approximation tool for hard decision problems

Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i

e

, with optimization problems with unknown but bounded data), of optimal control problems

What is the difference between convex and robust optimization?

Convex optimization: convexity, conic optimization, duality, KKT conditions

Robust optimization: robust optimization, chance constraints, applications

Here is the projected outline

Link to UC Berkeley Schedule of Classes: here

To communicate, we use bCourses

EE 227BT replaces the class previously offered as EE 227A


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