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.

Traditional variational quantum algorithms are often constrained by the optimization landscape, easily getting trapped in local minima when dealing with complex non-convex problems.

This paper deals with the packing problem of circles and non-convex polygons, which can be both translated and rotated into a strip with prohibited regions. Using the Ф-function technique, a ...

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 ...

We consider a non-stationary variant of a sequential stochastic optimization problem, in which the underlying cost functions may change along the horizon. We propose a measure, termed variation budget ...

Researchers from SJTU have developed a convex-optimization-based quantum process tomography method for reconstructing quantum channels, and have shown the validity to seawater channels and general ...

IEMS 459: Convex Optimization VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Linear Algebra, Calculus , Real Analysis Description The goal of this course is to investigate in-depth and to develop ...