Convex optimization ubc

Convex optimization is a key tool for analyzing and solving a range of computational problems that arise in machine learning, signal and image processing, theoretical computer science, and other fields. It is also forms the backbone for other areas of optimization, including nonconvex problems.

Convex optimization is a key tool for analyzing and solving a range of computational problems that arise in machine learning, signal and image processing,

Is dual function g a convex optimization problem?

Dual function g( ) is concave is a convex optimization problem

In this case, the optimal value d for the company is the cost under the least favorable set of prices ! max g( )

How well does the dual problem approximate the original problem? Weak Duality: optimal duality gap is always non-negative

Strong Duality: duality gap is zero

What is convex optimization?

Convex optimization is a key tool for analyzing and solving a range of computational problems that arise in machine learning, statistical signal and image processing, theoretical computer science, and other fields

It’s also the backbone for other areas of optimization, including algorithms for nonconvex problems

What skills are required for a convex optimization course?

A working knowledge of basic statistics and probability is also encouraged, although not necessary

Convex Optimization This course introduces fundamental principles and methods for optimization, which plays a central role in a variety of engineering problems

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