Optimization Letters covers all aspects of optimization, including theory, algorithms, computational studies, and applications. This journal provides an ...

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We consider (stochastic) subgradient methods for strongly convex but potentially nonsmooth non-Lipschitz optimization. We provide new equivalent dual descriptions (in the style of dual averaging) for ...

This work considers the nonconvex, nonsmooth problem of minimizing a composite objective of the form $f(g(x))+h(x)$ where the inner mapping $g$ is a smooth finite summation or expectation amenable to ...

Johns Hopkins researchers, including several affiliated with the Johns Hopkins Data Science and AI Institute, will present their research at tutorials, poster presentations, and workshops during the 2...

This work investigates stepsize-based acceleration of gradient descent with {\em anytime} convergence guarantees. For smooth (non-strongly) convex optimization, we propose a stepsize schedule that all...

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