arXiv:2604.02644v1 Announce Type: new
Abstract: We present Conditional Wasserstein Autoencoders (CWAEs), a framework for conditional simulation that exploits low-dimensional structure in both the conditioned and the conditioning variables. The key ide…
arXiv:2604.02738v1 Announce Type: cross
Abstract: This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this chal…
arXiv:2604.02917v1 Announce Type: cross
Abstract: We develop a sketch-based factor reduction and a Nesterov-accelerated projected gradient algorithm (NPGA) with GPU acceleration, yielding a doubly accelerated solver for large-scale constrained mean-va…
arXiv:2406.03099v4 Announce Type: replace
Abstract: This article explores the integration of deep learning models into combinatorial optimization pipelines, specifically targeting NP-hard problems. Traditional exact algorithms for such problems often …
arXiv:2510.06141v5 Announce Type: replace
Abstract: Convergence in high-probability (HP) has attracted increasing interest, due to implying exponentially decaying tail bounds and strong guarantees for individual runs of an algorithm. While many works …
arXiv:2604.02505v1 Announce Type: cross
Abstract: Recently, Jiang et al. [2026] developed Leon, a practical variant of One-sided Shampoo [Xie et al., 2025a, An et al., 2025] algorithm for online convex optimization, which does not require computing a …
arXiv:2505.17938v2 Announce Type: replace-cross
Abstract: Routing problems are canonical combinatorial optimization tasks with wide-ranging applications in logistics, transportation, and supply chain management. However, solving these problems becomes…
arXiv:2508.09103v3 Announce Type: replace-cross
Abstract: A quantum thermodynamic system is described by a Hamiltonian and a list of conserved, non-commuting charges, and a fundamental goal is to determine the minimum energy of the system subject to c…
arXiv:2603.28448v2 Announce Type: replace-cross
Abstract: We propose Yau’s Affine Normal Descent (YAND), a geometric framework for smooth unconstrained optimization in which search directions are defined by the equi-affine normal of level-set hypersur…
arXiv:2604.02666v1 Announce Type: new
Abstract: Optimization is as much about modeling the right problem as solving it. Identifying the right objectives, constraints, and trade-offs demands extensive interaction between researchers and stakeholders. L…