13 citations · 18 across the 5 of their papers we have counts for
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A unified analysis of convex and non-convex lp-ball projection problems
Joong-Ho Won, Kenneth Lange, Jason Xu
The task of projecting onto norm balls is ubiquitous in statistics and machine learning, yet the availability of actionable algorithms for doing so is largely limited to t…
Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions
Joong-Ho Won, Teng Zhang, Hua Zhou
This paper studies an optimization problem on the sum of traces of matrix quadratic forms in semi-orthogonal matrices, which can be considered as a generalization of the synchr…
Nonconvex Optimization via MM Algorithms: Convergence Theory
Kenneth Lange, Joong-Ho Won, Alfonso Landeros +1
The majorization-minimization (MM) principle is an extremely general framework for deriving optimization algorithms. It includes the expectation-maximization (EM) algorithm, proxim…
Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality
Joong-Ho Won, Hua Zhou, Kenneth Lange
This paper studies the problem of maximizing the sum of traces of matrix quadratic forms on a product of Stiefel manifolds. This orthogonal trace-sum maximization (OTSM) problem ge…
Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET
Ernest K. Ryu, Seyoon Ko, Joong-Ho Won
Many imaging problems, such as total variation reconstruction of X-ray computed tomography (CT) and positron-emission tomography (PET), are solved via a convex optimization problem…