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New PRISM framework enables Riemannian optimization on unknown manifolds

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new optimization framework called PRISM (Proximal Refinement on Implicit Smooth Manifolds) designed to work on unknown manifolds using only feasible samples. This method addresses the challenge of performing Riemannian optimization when tangent spaces and projections are not directly available. PRISM iteratively refines solutions by sampling from an objective-aware proximal distribution, theoretically ensuring objective descent, feasibility, and convergence to first-order stationarity. The framework has demonstrated effectiveness in synthetic experiments and real-world applications such as data-driven building control. AI

IMPACT This framework could enable more effective data-driven optimization in scientific and engineering domains by improving how models learn from limited feasible samples.

RANK_REASON The item is an academic paper detailing a new optimization framework. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PRISM framework enables Riemannian optimization on unknown manifolds

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The item is an academic paper detailing a new optimization framework. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ruixiang Wu, Jiahao Ai, Tinko Sebastian Bartels, Xuanzhuo Liu, Yifei Wu, Yu Mao, Tongxin Li, Costas Courcoubetis ·

    Riemannian Optimization on Unknown Manifolds via Stochastic Proximal Refinement

    arXiv:2610.41379v1 Announce Type: cross Abstract: We study Riemannian optimization on an unknown manifold accessed only through feasible samples. The central challenge is to turn feasible samples into optimization steps, i.e., the samples are drawn from the unknown feasible manif…

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