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New arXiv Papers Detail Advances in Convex Optimization Techniques

By PulseAugur Editorial · [5 sources] ·

Two new research papers on arXiv explore advancements in convex optimization. The first paper introduces a unified probing model for Online Convex Optimization (OCO) that can improve worst-case regret even with a sublinear and noisy probe budget. The second paper presents Hamiltonian dynamics-based algorithms that achieve accelerated convergence rates for smooth convex optimization, establishing Hamiltonian dynamics as a useful primitive for deterministic accelerated convex optimization. AI

RANK_REASON The cluster contains two academic papers published on arXiv detailing new research in optimization algorithms.

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AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New arXiv Papers Detail Advances in Convex Optimization Techniques

COVERAGE [5]

  1. arXiv cs.LG TIER_1 English(EN) · Taiqi Zhou, Weiyuan Gong ·

    Optimal Ansatz-free Hamiltonian Learning In Situ

    arXiv:2606.19486v1 Announce Type: cross Abstract: Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction. Recent works proposed protocols have achieved the o…

  2. arXiv cs.LG TIER_1 English(EN) · Simone Di Gregorio, Anupam Gupta, Stefano Leonardi, Matteo Russo ·

    Online Convex Optimization with Sublinear Noisy Probes

    arXiv:2606.14640v1 Announce Type: new Abstract: We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the …

  3. arXiv cs.LG TIER_1 English(EN) · Matteo Russo ·

    Online Convex Optimization with Sublinear Noisy Probes

    We study Online Convex Optimization (OCO) over a convex set $K\subseteq \mathbb R^d$, where in each round $t$ the learner selects $x_t\in K$ and then observes a convex loss $f_t:K\to[0,1]$, with the goal of minimizing regret to the best fixed decision in hindsight. We introduce a…

  4. arXiv stat.ML TIER_1 English(EN) · Xiuyuan Wang, Vishwak Srinivasan, Qiang Fu, Siddharth Mitra, Ashia Wilson, Andre Wibisono ·

    Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time

    arXiv:2606.17260v1 Announce Type: cross Abstract: We develop Hamiltonian dynamics-based algorithms for smooth convex optimization that achieve accelerated rates of convergence. By exploiting contraction of averaged Hamiltonian flow trajectories rather than requiring contraction a…

  5. arXiv stat.ML TIER_1 English(EN) · Andre Wibisono ·

    Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time

    We develop Hamiltonian dynamics-based algorithms for smooth convex optimization that achieve accelerated rates of convergence. By exploiting contraction of averaged Hamiltonian flow trajectories rather than requiring contraction at trajectory endpoints, we show that Hamiltonian d…

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