Public articles linked to the same research event.
arXiv Targeting the need for sparse transport plans in partial optimal transport (POT), this work proposes a penalty-based reformulation optimization framework that makes smooth strongly convex regularizers such as quadratic or elastic net usable for POT, and builds on it an accelerated first-order algorithm alternating between smooth updates and simple projection steps; on empirical benchmarks in color transfer, domain adaptation, and point cloud registration, the method consistently achieves lower transport cost, higher sparsity, and faster convergence than established baselines.
Targeting the need for sparse transport plans in partial optimal transport (POT), this work proposes a penalty-based reformulation optimization framework that makes smooth strongly convex regularizers such as quadratic or elastic net usable for POT, and builds on it an accelerated first-order algorithm alternating between smooth updates and simple projection steps; on empirical benchmarks in color transfer, domain adaptation, and point cloud registration, the method consistently achieves lower transport cost, higher sparsity, and faster convergence than established baselines.
Targeting the need for sparse transport plans in partial optimal transport (POT), this work proposes a penalty-based reformulation optimization framework that makes smooth strongly convex regularizers such as quadratic or elastic net usable for POT, and builds on it an accelerated first-order algorithm alternating between smooth updates and simple projection steps; on empirical benchmarks in color transfer, domain adaptation, and point cloud registration, the method consistently achieves lower transport cost, higher sparsity, and faster convergence than established baselines.
Targeting the need for sparse transport plans in partial optimal transport (POT), this work proposes a penalty-based reformulation optimization framework that makes smooth strongly convex regularizers such as quadratic or elastic net usable for POT, and builds on it an accelerated first-order algorithm alternating between smooth updates and simple projection steps; on empirical benchmarks in color transfer, domain adaptation, and point cloud registration, the method consistently achieves lower transport cost, higher sparsity, and faster convergence than established baselines.