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math.OC2024

Sharpness and well-conditioning of nonsmooth convex formulations in statistical signal recovery

Lijun Ding, Alex L. Wang

We study a sample complexity vs. conditioning tradeoff in modern signal recovery problems (including sparse recovery, low-rank matrix sensing, covariance estimation, and abstract p…

math.OC2024

New notions of simultaneous diagonalizability of quadratic forms with applications to QCQPs

Alex L. Wang, Rujun Jiang

A set of quadratic forms is simultaneously diagonalizable via congruence (SDC) if there exists a basis under which each of the quadratic forms is diagonal. This property appears na…

math.OC2024

Hidden convexity, optimization, and algorithms on rotation matrices

Akshay Ramachandran, Kevin Shu, Alex L. Wang

This paper studies hidden convexity properties associated with constrained optimization problems over the set of rotation matrices . Such problems are nonconvex due t…

math.OC2024

On semidefinite descriptions for convex hulls of quadratic programs

Alex L. Wang, Fatma Kilinc-Karzan

Quadratically constrained quadratic programs (QCQPs) are a highly expressive class of nonconvex optimization problems. While QCQPs are NP-hard in general, they admit a natural conv…

math.OC2024

Accelerated first-order methods for a class of semidefinite programs

Alex L. Wang, Fatma Kilinc-Karzan

This paper introduces a new storage-optimal first-order method (FOM), CertSDP, for solving a special class of semidefinite programs (SDPs) to high accuracy. The class of SDPs that…