Averaged Mirror Descent and Dual Gradient Methods: Convergent Algorithms for Entropic Gromov-Wasserstein Problem

arXiv:2609.31848 · cs.LG, math.OC · Submitted 2026-09-25 · Read on arXiv

cs.LG, math.OC

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• alpha — 32× in this paper · explained in 1 episode
• Mirror Descent — 25× in this paper
• Averaged Mirror Descent — 20× in this paper
• Regularization — 15× in this paper · explained in 3 episodes
• Optimal Transport — 9× in this paper · explained in 5 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Wasserstein Distance — 5× in this paper · explained in 4 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Point Clouds — 4× in this paper · explained in 2 episodes
• Quadratic Program — 4× in this paper
• Aggregation — 2× in this paper · explained in 1 episode
• Entropic Optimal Transport — 2× in this paper
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes

Sources

• Sliced Inner Product Gromov-Wasserstein Distances
• Approximation Analysis of the Entropic Penalty in Quadratic Programming
• Limit Laws for Gromov-Wasserstein Alignment with Applications to Testing Graph Isomorphisms
• Discrete Gromov-Wasserstein Duality: Algorithms and Isomorphism Testing

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