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20152026
most citedStatistical Learning with Conditional Value at Risk

22 citations · 63 across the 20 of their papers we have counts for

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6 papers · 1 filter

math.OC2026

Quiver Semistability and Structured Kalman Decompositions for Networked Linear Dynamical Systems

Kazuo Murota, Tasuku Soma

We introduce new notions of controllability and observability for networked linear time-invariant (LTI) systems based on -semistability of quiver representations. Utilizing King…

math.OC2026

Numerically stable variants of overrelaxation for operator Sinkhorn iteration

Henrik Eisenmann, Tasuku Soma, Xun Tang +1

We consider accelerated versions of the operator Sinkhorn iteration (OSI) for solving scaling problems for completely positive maps. Based on the interpretation of OSI as alternati…

math.OC2024

Accelerating operator Sinkhorn iteration with overrelaxation

Tasuku Soma, André Uschmajew

We propose accelerated versions of the operator Sinkhorn iteration for operator scaling using successive overrelaxation. We analyze the local convergence rates of these accelerated…

math.OC2024

Algorithmic aspects of semistability of quiver representations

Yuni Iwamasa, Taihei Oki, Tasuku Soma

We study the semistability of quiver representations from an algorithmic perspective. We present efficient algorithms for several fundamental computational problems on the semistab…

math.OC2022

Algebraic Algorithms for Fractional Linear Matroid Parity via Non-commutative Rank

Taihei Oki, Tasuku Soma

Matrix representations are a powerful tool for designing efficient algorithms for combinatorial optimization problems such as matching, and linear matroid intersection and parity.…

math.OC2020

Information geometry of operator scaling

Takeru Matsuda, Tasuku Soma

Matrix scaling is a classical problem with a wide range of applications. It is known that the Sinkhorn algorithm for matrix scaling is interpreted as alternating e-projections from…