Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Constrained optimization problems have appeared in a wide variety of challenging real-
world problems, where constraints often capture the physics of the underlying system …

Homotopy optimization is a traditional method to deal with a complicated optimization
problem by solving a sequence of easy-to-hard surrogate subproblems. However, this …

In this letter, we consider an optimization problem with box constraints coupled with polytope
constraints. The existing deep learning methodologies for solving such constrained …

The evolution towards a more distributed and interconnected grid necessitates large-scale
decision-making within strict temporal constraints. Machine learning (ML) paradigms have …

Incorporating a deep generative model as the prior distribution in inverse problems has
established substantial success in reconstructing images from corrupted observations …

This paper conducts a thorough comparative analysis of optimization algorithms for an
unconstrained convex optimization problem. It contrasts traditional methods like Gradient …

Deep neural networks are recognized as a promising approach for solving non-convex
problems related to resource allocation in wireless communication systems. This letter …

The ever-increasing integration of stochastic renewable energy sources into power systems
operation is making the supply-demand balance more challenging. While joint chance …

For parameterized mixed-binary optimization problems, we construct local decision rules
that prescribe near-optimal courses of action across a set of parameter values. The decision …