The paper establishes existence, uniqueness up to equivalence, and stability for inverse optimal transport with Bregman regularization under cost-matrix assumptions, and gives an efficient BCD algorithm with linear convergence and element-wise Newton updates for quadratic penalties.
Sis ta: learning optimal transport costs under sparsity constraints
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Well-Posedness and Efficient Algorithms for Inverse Optimal Transport with Bregman Regularization
The paper establishes existence, uniqueness up to equivalence, and stability for inverse optimal transport with Bregman regularization under cost-matrix assumptions, and gives an efficient BCD algorithm with linear convergence and element-wise Newton updates for quadratic penalties.