Solving non convex non smooth problems is a big challenge involving saddle point localization, non differentialibilty, escape from saddle point and others. Proximal algorithms and its variants ...

This chapter helps the students to identify convex functions, convex sets, and convex optimization problems. It presents comparison between a convex and a non‐convex function. The chapter discusses ...

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 this paper we have considered a non convex optimal control problem and presented the weak, strong and converse duality theorems. The optimality conditions and duality theorems for fractional ...

We study distributed non-convex optimization on a time-varying multi-agent network. Each node has access to its own smooth local cost function, and the collective goal is to minimize the sum of these ...

The objective functions are usually multi objective. The constraints are convex, concave or non convex in nature. In [1] -[3] , the authors have established both theoretical and applied results ...