Non-convex optimization is now ubiquitous in machine learning. While previously, the focus was on convex relaxation methods, now the emphasis is on being able to solve non-convex problems directly.

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. About the Purdue ...

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...

Course Description. This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; ...

In business, much to the data scientist’s pleasure, so much of optimization is in finding an even narrower local maximum or minimum. That’s a key reason why deep learning systems are of such ...

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. About the Purdue ...