This course lays the theoretical and algorithmic foundations of convex optimization problems. We provide a fairly general understanding of a wide class of problems including linear programming, quadratic programming, and geometric programming.

In this seminar, we discuss one recent work at the interface of applied mathematics and machine learning with the goal of exposing new research questions.

In this seminar, we discuss one recent work at the interface of applied mathematics and machine learning with the goal of exposing new research questions.

Interactive three-hour mini-course held most recently in the 2021 Spring School on Models and Data, University of South Carolina.

This course, which is part two of our three-part graduate sequence on numerical analysis, focusses on optimization, root finding, interpolation, differentiation, integration, and differential equations.

© Lars Ruthotto 2022