Learning Deep Models : Critical Points and Local Openness
With the increasing popularity of nonconvex deep models, developing a unifying theory for studying the optimization problems that arise from training these models becomes very significant. Toward t…
A Class of Convex Quadratic Nonseparable Resource Allocation Problems with Ge…
We study a convex quadratic nonseparable resource allocation problem that arises in the area of decentralized energy management (DEM), where unbalance in electricity networks has to be minimized. I…
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Applica…
Spectral clustering is one of the fundamental unsupervised learning methods and is widely used in data analysis. Sparse spectral clustering (SSC) imposes sparsity to the spectral clustering, and it…
Learning in Sequential Bilevel Linear Programming
We consider a framework for sequential bilevel linear programming in which a leader and a follower interact over multiple time periods. In each period, the follower observes the actions taken by th…
A Solution Approach to Distributionally Robust Joint-Chance-Constrained Assig…
We study the assignment problem with chance constraints (CAP) and its distributionally robust counterpart DR-CAP. We present a technique for estimating big-M in such a formulation that takes advant…
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