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A verifiable and accurate termination criterion for PWS optimization is introduced and like the gradient norm in smooth optimization, this certificate tightly characterizes the optimality gap under the QG condition and can be evaluated without knowledge of any problem parameters.
We introduce an anytime, almost parameter-free algorithm for convex function-constrained optimization, in which the objective and constraint functions are unknown piecewise-smooth. Our algorithm, Restarted Penalty APEX (Penalty Accelerated Prox-level method for Exploring Piecewise Smoothness), is an accelerated bundle-...
We study deterministic first-order minimization of a convex function without prior knowledge of the objective's growth, smoothness regime, or associated parameters. We develop anytime, parameter-free bundle-level methods that adapt simultaneously to these unknown properties and attain best-known oracle complexities. Fo...
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