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Mirror descent algorithms with logarithmic barriers

De Marchi, Alberto et al.
arXiv (All) · Papers
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machine-learningnumerical-analysisoptimization-and-control
We derive convergence guarantees for mirror descent and proximal mirror descent with a logarithmic barrier as the distance-generating function. When the solution lies on the boundary, the Bregman divergence blows up and classical guarantees become vacuous. While existing remedies recover $O(\log k/k
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