Semidefinite Relaxations of the Gromov-Wasserstein Distance
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Junyu, Nguyen, Binh T., Koh, Shang Hui, Soh, Yong Sheng |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exactness Conditions for Semidefinite Relaxations of the Quadratic Assignment Problem
by: Chen, Junyu, et al.
Published: (2024)
by: Chen, Junyu, et al.
Published: (2024)
Sum-of-Squares Hierarchy for the Gromov Wasserstein Problem
by: Tran, Hoang Anh, et al.
Published: (2025)
by: Tran, Hoang Anh, et al.
Published: (2025)
Moment Sum-of-Squares Hierarchy for Gromov Wasserstein: Continuous Extensions and Sample Complexity
by: Tran, Hoang Anh, et al.
Published: (2025)
by: Tran, Hoang Anh, et al.
Published: (2025)
Sliced Inner Product Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2026)
by: Gong, Xiaoyun, et al.
Published: (2026)
A Novel Sliced Fused Gromov-Wasserstein Distance
by: Piening, Moritz, et al.
Published: (2025)
by: Piening, Moritz, et al.
Published: (2025)
A Relaxed Wasserstein Distance Formulation for Mixtures of Radially Contoured Distributions
by: Chen, Keyu, et al.
Published: (2025)
by: Chen, Keyu, et al.
Published: (2025)
Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance
by: Nakashima, Haruto, et al.
Published: (2025)
by: Nakashima, Haruto, et al.
Published: (2025)
Linear Partial Gromov-Wasserstein Embedding
by: Bai, Yikun, et al.
Published: (2024)
by: Bai, Yikun, et al.
Published: (2024)
Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow
by: Qiu, Guancheng, et al.
Published: (2025)
by: Qiu, Guancheng, et al.
Published: (2025)
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
by: Anderson, Brendon G., et al.
Published: (2021)
by: Anderson, Brendon G., et al.
Published: (2021)
Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)
by: Le, Bach C., et al.
Published: (2025)
A Provably Convergent and Practical Algorithm for Gromov--Wasserstein Optimal Transport
by: Liang, Ling, et al.
Published: (2026)
by: Liang, Ling, et al.
Published: (2026)
Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
by: Seyedi, Iman, et al.
Published: (2025)
by: Seyedi, Iman, et al.
Published: (2025)
Formation Shape Control using the Gromov-Wasserstein Metric
by: Nakashima, Haruto, et al.
Published: (2025)
by: Nakashima, Haruto, et al.
Published: (2025)
Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)
by: Zhang, Zhengxin, et al.
Published: (2024)
Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
by: Wang, Lei, et al.
Published: (2025)
by: Wang, Lei, et al.
Published: (2025)
Generalized Dimension Reduction Using Semi-Relaxed Gromov-Wasserstein Distance
by: Clark, Ranthony A., et al.
Published: (2024)
by: Clark, Ranthony A., et al.
Published: (2024)
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
by: Chemseddine, Jannis, et al.
Published: (2024)
by: Chemseddine, Jannis, et al.
Published: (2024)
Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026)
by: Nguyen, Duc Toan, et al.
Published: (2026)
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization
by: Garber, Dan, et al.
Published: (2024)
by: Garber, Dan, et al.
Published: (2024)
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
by: Tran, Trinh, et al.
Published: (2026)
by: Tran, Trinh, et al.
Published: (2026)
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
by: Zhuang, Yubo, et al.
Published: (2023)
by: Zhuang, Yubo, et al.
Published: (2023)
A Semidefinite Programming-Based Branch-and-Cut Algorithm for Biclustering
by: Sudoso, Antonio M.
Published: (2024)
by: Sudoso, Antonio M.
Published: (2024)
Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space
by: Yang, Sheng, et al.
Published: (2024)
by: Yang, Sheng, et al.
Published: (2024)
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
by: Angell, Rico, et al.
Published: (2023)
by: Angell, Rico, et al.
Published: (2023)
Mixtures Closest to a Given Measure: A Semidefinite Programming Approach
by: Đurašinović, Srećko, et al.
Published: (2025)
by: Đurašinović, Srećko, et al.
Published: (2025)
Entropic Gromov-Wasserstein Distances: Stability and Algorithms
by: Rioux, Gabriel, et al.
Published: (2023)
by: Rioux, Gabriel, et al.
Published: (2023)
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
by: Kuhn, Daniel, et al.
Published: (2019)
by: Kuhn, Daniel, et al.
Published: (2019)
Distributional Surgery for Language Model Activations
by: Nguyen, Bao, et al.
Published: (2025)
by: Nguyen, Bao, et al.
Published: (2025)
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)
by: Bonet, Clément, et al.
Published: (2025)
Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)
by: Chen, Guixian, et al.
Published: (2026)
The Star Geometry of Critic-Based Regularizer Learning
by: Leong, Oscar, et al.
Published: (2024)
by: Leong, Oscar, et al.
Published: (2024)
Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
by: Leong, Oscar, et al.
Published: (2025)
by: Leong, Oscar, et al.
Published: (2025)
Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
by: Cory-Wright, Ryan, et al.
Published: (2025)
by: Cory-Wright, Ryan, et al.
Published: (2025)
Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity
by: Zhu, Shuailong, et al.
Published: (2025)
by: Zhu, Shuailong, et al.
Published: (2025)
Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
by: Wang, Yikai, et al.
Published: (2026)
by: Wang, Yikai, et al.
Published: (2026)
Centering ADMM for the Semidefinite Relaxation of the QAP
by: Kanoh, Shin-ichi, et al.
Published: (2020)
by: Kanoh, Shin-ichi, et al.
Published: (2020)
Decentralized Stochastic Nonconvex Optimization under the Relaxed Smoothness
by: Luo, Luo, et al.
Published: (2025)
by: Luo, Luo, et al.
Published: (2025)
Wasserstein Distributionally Robust Regret Optimization
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)
Control and optimization for Neural Partial Differential Equations in Supervised Learning
by: Bensoussan, Alain, et al.
Published: (2025)
by: Bensoussan, Alain, et al.
Published: (2025)
Similar Items
-
Exactness Conditions for Semidefinite Relaxations of the Quadratic Assignment Problem
by: Chen, Junyu, et al.
Published: (2024) -
Sum-of-Squares Hierarchy for the Gromov Wasserstein Problem
by: Tran, Hoang Anh, et al.
Published: (2025) -
Moment Sum-of-Squares Hierarchy for Gromov Wasserstein: Continuous Extensions and Sample Complexity
by: Tran, Hoang Anh, et al.
Published: (2025) -
Sliced Inner Product Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2026) -
A Novel Sliced Fused Gromov-Wasserstein Distance
by: Piening, Moritz, et al.
Published: (2025)