arXiv:2604.00817v1 Announce Type: new
Abstract: Detecting and delineating tiny targets in 3D brain scans is a central yet under-addressed challenge in medical imaging.In ischemic stroke, for instance, the culprit thrombus is small, low-contrast, and v…
arXiv:2505.19574v3 Announce Type: replace-cross
Abstract: Autonomous robots operating in complex, unstructured environments face significant challenges due to latent, unobserved factors that obscure their understanding of both their internal state and…
arXiv:2505.00213v3 Announce Type: replace
Abstract: While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increases with the number o…
arXiv:2604.00364v1 Announce Type: cross
Abstract: This paper introduces a new method for solving quadratic programs using primal-dual interior-point methods. Instead of handling complementarity as an explicit equation in the Karush-Kuhn-Tucker (KKT) c…
arXiv:2604.01034v1 Announce Type: new
Abstract: We propose a Stein variational distributionally robust controller for nonlinear dynamical systems with latent parametric uncertainty. The method is an alternative to conservative worst-case ambiguity-set…
arXiv:2603.29117v1 Announce Type: cross
Abstract: Due to simplicity and strong stability guarantees, predictor feedback methods have stood as a popular approach for time delay systems since the 1950s. For time-varying delays, however, implementation r…
arXiv:2603.29119v1 Announce Type: cross
Abstract: Modern control systems frequently operate under input delays and sampled state measurements. A common delay-compensation strategy is predictor feedback; however, practical implementations require solvi…
arXiv:2603.29128v1 Announce Type: cross
Abstract: Cyclic block coordinate methods are a fundamental class of first-order algorithms, widely used in practice for their simplicity and strong empirical performance. Yet, their theoretical behavior remains…
arXiv:2209.03282v4 Announce Type: replace-cross
Abstract: Accelerating the convergence of second-order optimization, particularly Newton-type methods, remains a pivotal challenge in algorithmic research. In this paper, we extend previous work on the \…
arXiv:2410.23362v2 Announce Type: replace-cross
Abstract: The non-convex nature of trained neural networks has created significant obstacles in their incorporation into optimization models. In this context, Anderson et al. (2020) provided a framework …