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 ...

Mathematical optimization and machine learning are two tools that, at first glance, may seem to have a lot in common.

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

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

The goal is to design a large scale optimization algorithm for machine learning tasks that can run on decentralized compute environments (such as peer-to-peer systems). Requires ability to program and ...

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 ...

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