descent method
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Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
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Stochastic Spectral and Conjugate Descent Methods
Dmitry Kovalev, Peter Richtarik, Eduard Gorbunov, Elnur Gasanov
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A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
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Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.
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Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Recently, researchers from convex optimization proposed the notions of "relative Lipschitz continuity" and "relative strong convexity". Both of the notions are generalizations oftheirclassicalcounterparts.
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02f657d55eaf1c4840ce8d66fcdaf90c-AuthorFeedback.pdf
In the following, we respond to all the reviewers' questions that will be addressed in the paper's final version together W e would also like to point out t hat the bounds' tightness is shown not only in Fig.1 but However, as the Reviewer ment ions, the paper offers new insights for feature mappings' Those with large number of samples were used for co mparison with performance bounds in Fig.1 over one Theorem 1. Infinite instance spaces would require to use heav ier tools from variational analysis in such proof, but the