IEMS 458: Convex Optimization VIEW ALL COURSE TIMES AND SESSIONS Prerequisites 450-2 is recommended but not required Description The course will take an in-depth look at the main concepts and ...

From a mathematical and computer science point of view, the biggest advantage of this three-dimensional embedding is that image processing problems can be solved using convex optimization algorithms.

Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations.

In particular, we show that the reformulation of the Kiefer–Wolfowitz estimator as a convex optimization problem reduces the computational effort by several orders of magnitude for typical problems, ...

Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations.

Even without convexity, this algorithm can be generically used as an oracle-efficient optimization algorithm, with accuracy evaluated empirically. We complement our theoretical results with an ...

The portfolio optimization model has limited impact in practice because of estimation issues when applied to real data. To address this, we adapt two machine learning methods, regularization and cross ...