# CMU 10-725 Convex Optimization

Carnegie Mellon University,, Fall 2018 , Prof. Ryan Tibshirani

Carnegie Mellon University,, Fall 2018 , Prof. Ryan Tibshirani

Nearly every problem in machine learning and computational statistics can be formulated

in terms of the optimization of some function, possibly under some set of constraints. As

we obviously cannot solve every problem in machine learning, this means that we cannot

generically solve every optimization problem (at least not efficiently). Fortunately, many

problems of interest in machine learning can be posed as optimization tasks that have

special propertiessuch as convexity, smoothness, sparsity, separability, etc.permitting

standardized, efficient solution techniques.

This course is designed to give a graduate-level student a thorough grounding in these

properties and their role in optimization, and a broad comprehension of algorithms tailored

to exploit such properties. The focus will be on convex optimization problems (though

we also may touch upon nonconvex optimization problems at some points). We will visit

and revisit important applications in machine learning and statistics.

properties and their role in optimization, and a broad comprehension of algorithms tailored

to exploit such properties. The focus will be on convex optimization problems (though

we also may touch upon nonconvex optimization problems at some points). We will visit

and revisit important applications in machine learning and statistics.

Upon completing the

course, students should be able to approach an optimization problem (often derived from a

machine learning or statistics context) and:

course, students should be able to approach an optimization problem (often derived from a

machine learning or statistics context) and:

- 1. identify key properties such as convexity, smoothness, sparsity, etc., and/or possibly

reformulate the problem so that it possesses such desirable properties; - 2. select an algorithm for this optimization problem, with an understanding of the advantages and disadvantages of applying one method over another, given the problem

and properties at hand; - 3. implement this algorithm or use existing software to efficiently compute the solution.

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4.1 ( 11 )

Sam

Sep 12, 2018

Excellent course helped me understand topic that i couldn't while attendinfg my college.

Dembe

March 29, 2019

Great course. Thank you very much.