collaborators

10 papers

math.OC2026

Continuous-Time Covariance Steering with Common Free-Final Time: Finite-Horizon Solutions and Infinite-Horizon Limits

Akan Selim, Fengjiao Liu, Siddhartha Ganguly +1

This article studies the optimal common free-final time problem for steering the state covariance of a continuous-time stochastic linear system between prescribed initial and termi…

math.OC2026

Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions

Siddhartha Ganguly, George Rapakoulias, Panagiotis Tsiotras

We study stochastic density control between Gaussian-mixture endpoint distributions under Brownian prior dynamics. Since the direct Schrödinger bridge between Gaussian mixtures is…

math.OC2026

OT-DETECT: Optimal Transport-Driven Attack Detection in Cyber-Physical Systems

Souvik Das, Siddhartha Ganguly

This letter presents an optimal-transport (OT)-driven, distributionally robust attack detection algorithm, OT-DETECT, for cyber-physical systems (CPS) modeled as partially observed…

math.OC2026

Unbalanced Optimal Transport and Density Control for Discrete-Time Linear Systems

Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima

This article studies unbalanced optimal transport (UOT) and its dynamical extension, unbalanced density control (UDC), for a class of constrained discrete-time linear systems. UOT…

math.OC2026

Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions

Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima

In this article, we study unbalanced optimal transport (UOT) and establish a control-theoretic dynamical extension, which we call the unbalanced density control (UDC), for a class…

math.OC2026

Covariance Steering of Discrete-Time Markov Jump Linear Systems with Multiplicative Noise

Fangji Wang, Siddhartha Ganguly, Panagiotis Tsiotras

We study a finite-horizon covariance steering problem for discrete-time Markov jump linear systems (MJLS) with both state- and control-dependent multiplicative noise. The objective…