Convex optimization cmu

In convex problems the graph of the objective function and the feasible set are both convex (where a set is convex if a line joining any two points in the set is contained in the set).
Another special case is quadratic programming, in which the constraints…
You should have good knowledge of linear algebra and exposure to probability.
Exposure to numerical computing, optimization, and application fields is helpful but not required; the applications will be kept basic and simple.

Convex Optimization: Fall 2019Machine Learning 10-725ScheduleHomeworkReview aids.

What is 10725 - convex optimization?

10725 - Convex Optimization Nearly every problem in machine learning can be formulated as the optimization of some function, possibly under some set of constraints

This universal reduction may seem to suggest that such optimization tasks are intractable

What is convex optimization?

Course descriptions: According to Wikipedia: Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets

Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard

Who is the Head TA for convex optimization?

Convex Optimization: Fall 2018 Machine Learning 10-725 Instructor:Ryan Tibshirani(ryantibs at cmu dot edu) Important note: please direct emails on all course related matters to the Head TA, not the Instructor

The subject line of all emails should begin with "[10-725]"

Head TA:Po-Wei Wang(poweiw at andrew dot cmu dot edu) TAs:

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