arXiv:2604.01472v1 Announce Type: cross
Abstract: The Muon optimizer has received considerable attention for its strong performance in training large language models, yet the design principle behind its matrix-gradient orthogonalization remains largel…
arXiv:2604.01525v1 Announce Type: cross
Abstract: We give a short linear–algebraic proof of the inequality \[ \|x\|_1\,\|x\|_\infty \le \frac{1+\sqrt{p}}{2}\,\|x\|_2^2, \] valid for every \(x\in\mathbb{R}^p\). This inequality relates three fundamenta…
arXiv:2604.01606v1 Announce Type: cross
Abstract: Optimization over the space of probability measures endowed with the Wasserstein-2 geometry is central to modern machine learning and mean-field modeling. However, traditional methods relying on full W…
arXiv:2506.15387v2 Announce Type: replace-cross
Abstract: We propose two variants of the Primal Dual Hybrid Gradient (PDHG) algorithm for saddle point problems with block decomposable duals, hereafter called Multi-Timescale PDHG (MT-PDHG) and its acce…
arXiv:2604.01912v1 Announce Type: cross
Abstract: This work formalizes the differential topology of redundancy resolution for systems governed by signed-quadratic actuation maps. By analyzing the minimally redundant case, the global topology of the co…
arXiv:2604.02035v1 Announce Type: cross
Abstract: We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang et al. [2020]. The problem is formulated as a sequential optimal stopping problem over entry …
arXiv:2604.01308v1 Announce Type: new
Abstract: Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic ope…
arXiv:2601.11016v2 Announce Type: replace-cross
Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing…
arXiv:2604.01378v1 Announce Type: new
Abstract: Offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real environment, which is particularly important i…
arXiv:2604.02290v1 Announce Type: new
Abstract: Surface registration plays an important role for anatomical shape analysis in medical imaging. Existing surface registration methods often face a trade-off between efficiency and robustness. Local point …