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math.OC2026
Gradient-Only Online Convex Optimization with a Single Quadratic Leader
Kamiar Asgari
We study online convex optimization on a bounded Euclidean domain when the learner receives only one subgradient at its prediction and knows neither the horizon, a gradient bound,…
math.OC2023
Nonsmooth Projection-Free Optimization with Functional Constraints
Kamiar Asgari, Michael J. Neely
This paper presents a subgradient-based algorithm for constrained nonsmooth convex optimization that does not require projections onto the feasible set. While the well-established…
math.OC2022★ 1 cited
Projection-Free Non-Smooth Convex Programming
Kamiar Asgari, Michael J. Neely
We study nonsmooth convex minimization over a compact convex set without projections. For a globally -Lipschitz objective, optimistic primal-dual updates give error at most $2LD…