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math.OC2026
Efficient Optimization on the Euclidean Sphere: Riemannian Gradient Alignment as a Unifying Principle
Puqian Wang, Nikos Zarifis, Jelena Diakonikolas
Euclidean sphere-constrained optimization problems represent perhaps the most basic class of nonconvex-constrained problems for which there exist efficient optimization algorithms…
math.OC2024
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Shuyao Li, Yu Cheng, Ilias Diakonikolas +3
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learnin…
math.OC2021★ 7 cited
Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization
Jelena Diakonikolas, Puqian Wang
Making the gradients small is a fundamental optimization problem that has eluded unifying and simple convergence arguments in first-order optimization, so far primarily reserved fo…