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Center for Nonlinear Studies |
Versatile and efficient non-linear optimization algorithms and software have made optimization models a viable alternative to closed form formulae for large and complex problems. We will focus on two areas in modern convex optimization that have received an increased interest recently, owing to a growing number of applications. First, optimization with shape constraints using conic programming (and more specifically, using ``sum of squares'' cones) will be presented. Applications include function estimation problems, such as those in statistics and engineering. In the second part of the talk we will see how to use stochastic optimization models with coherent risk measures for risk-averse decision making.
I will present a broad overview of the basics of convex optimization.
Prior knowledge of the theory of optimization is not needed.
Host: Feng Pan