9 citations · 21 across the 19 of their papers we have counts for
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Quadratic Optimization over Probability Measures with Coupling Constraints
Hoang Anh Tran, Yong Sheng Soh
We consider solving an optimization instance in which the objective is quadratic and where the decision variable is a probability measure. Our class of problems are motivated by ap…
Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
Regularization is a central tool for addressing ill-posedness in inverse problems and statistical estimation, with the choice of a suitable penalty often determining the reliabilit…
Moment Sum-of-Squares Hierarchy for Gromov Wasserstein: Continuous Extensions and Sample Complexity
Hoang Anh Tran, Binh Tuan Nguyen, Yong Sheng Soh
The Gromov-Wasserstein (GW) problem is an extension of the classical optimal transport problem to settings where the source and target distributions reside in incomparable spaces,…
Sum-of-Squares Hierarchy for the Gromov Wasserstein Problem
Hoang Anh Tran, Binh Tuan Nguyen, Yong Sheng Soh
The Gromov-Wasserstein (GW) problem is a variant of the classical optimal transport problem that allows one to compute meaningful transportation plans between incomparable spaces.…
Exactness Conditions for Semidefinite Relaxations of the Quadratic Assignment Problem
Junyu Chen, Yong Sheng Soh
The Quadratic Assignment Problem (QAP) is an important discrete optimization instance that encompasses many well-known combinatorial optimization problems, and has applications in…
Semidefinite Relaxations of the Gromov-Wasserstein Distance
Junyu Chen, Binh T. Nguyen, Shang Hui Koh +1
The Gromov-Wasserstein (GW) distance is an extension of the optimal transport problem that allows one to match objects between incomparable spaces. At its core, the GW distance is…
The Lovász Theta Function for Recovering Planted Clique Covers and Graph Colorings
Jiaxin Hou, Yong Sheng Soh, Antonios Varvitsiotis
The problems of computing graph colorings and clique covers are central challenges in combinatorial optimization. Both of these are known to be NP-hard, and thus computationally in…
Dictionary Learning under Symmetries via Group Representations
Subhroshekhar Ghosh, Aaron Y. R. Low, Yong Sheng Soh +2
The dictionary learning problem can be viewed as a data-driven process to learn a suitable transformation so that data is sparsely represented directly from example data. In this p…
Optimal Regularization for a Data Source
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh +1
In optimization-based approaches to inverse problems and to statistical estimation, it is common to augment criteria that enforce data fidelity with a regularizer that promotes des…
Multiplicative updates for symmetric-cone factorizations
Yong Sheng Soh, Antonios Varvitsiotis
Given a matrix with non-negative entries, the cone factorization problem over a cone concerns computing $\{ a_1,…
A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations
Yong Sheng Soh, Antonios Varvitsiotis
Given a matrix with nonnegative entries, a Positive Semidefinite (PSD) factorization of is a collection of -dimensional PSD matrices…
A Note on Convex Relaxations for the Inverse Eigenvalue Problem
Utkan Candogan, Yong Sheng Soh, Venkat Chandrasekaran
The affine inverse eigenvalue problem consists of identifying a real symmetric matrix with a prescribed set of eigenvalues in an affine space. Due to its ubiquity in applications,…
Learning Semidefinite Regularizers
Yong Sheng Soh, Venkat Chandrasekaran
Regularization techniques are widely employed in optimization-based approaches for solving ill-posed inverse problems in data analysis and scientific computing. These methods are b…