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@arXiv_mathCT_bot@mastoxiv.page
2025-09-15 08:47:11

A Categorical Approach to Finiteness Conditions
David Forsman
arxiv.org/abs/2509.10204 arxiv.org/pdf/2509.10204

@arXiv_mathLO_bot@mastoxiv.page
2025-10-14 07:44:32

Computable Bases
Vasco Brattka, Emmanuel Rauzy
arxiv.org/abs/2510.09850 arxiv.org/pdf/2510.09850

@arXiv_eessSY_bot@mastoxiv.page
2025-10-14 10:33:28

Controller for Incremental Input-to-State Practical Stabilization of Partially Unknown systems with Invariance Guarantees
P Sangeerth, David Smith Sundarsingh, Bhabani Shankar Dey, Pushpak Jagtap
arxiv.org/abs/2510.10450

@arXiv_mathAC_bot@mastoxiv.page
2025-10-14 08:39:38

Uniformly-S-pseudo-projective modules
Mohammad adarbeh, Mohammad Saleh
arxiv.org/abs/2510.10170 arxiv.org/pdf/2510.10170

@arXiv_csCL_bot@mastoxiv.page
2025-10-15 10:46:51

Hey, wait a minute: on at-issue sensitivity in Language Models
Sanghee J. Kim, Kanishka Misra
arxiv.org/abs/2510.12740 arxiv.org/pdf/2510.1…

@arXiv_quantph_bot@mastoxiv.page
2025-10-15 10:27:31

Multi-Copy Security in Unclonable Cryptography
Alper \c{C}akan, Vipul Goyal, Fuyuki Kitagawa, Ryo Nishimaki, Takashi Yamakawa
arxiv.org/abs/2510.12626

@arXiv_mathOC_bot@mastoxiv.page
2025-11-14 10:04:30

Verification of Sequential Convex Programming for Parametric Non-convex Optimization
Rajiv Sambharya, Nikolai Matni, George Pappas
arxiv.org/abs/2511.10622 arxiv.org/pdf/2511.10622 arxiv.org/html/2511.10622
arXiv:2511.10622v1 Announce Type: new
Abstract: We introduce a verification framework to exactly verify the worst-case performance of sequential convex programming (SCP) algorithms for parametric non-convex optimization. The verification problem is formulated as an optimization problem that maximizes a performance metric (e.g., the suboptimality after a given number of iterations) over parameters constrained to be in a parameter set and iterate sequences consistent with the SCP update rules. Our framework is general, extending the notion of SCP to include both conventional variants such as trust-region, convex-concave, and prox-linear methods, and algorithms that combine convex subproblems with rounding steps, as in relaxing and rounding schemes. Unlike existing analyses that may only provide local guarantees under limited conditions, our framework delivers global worst-case guarantees--quantifying how well an SCP algorithm performs across all problem instances in the specified family. Applications in control, signal processing, and operations research demonstrate that our framework provides, for the first time, global worst-case guarantees for SCP algorithms in the parametric setting.
toXiv_bot_toot

@arXiv_mathCV_bot@mastoxiv.page
2025-10-14 07:42:31

Division algebras of slice-Nash functions
Cinzia Bisi, Antonio Carbone
arxiv.org/abs/2510.09779 arxiv.org/pdf/2510.09779

@arXiv_mathAG_bot@mastoxiv.page
2025-09-15 08:46:11

On defectivity of joins, reducible secants and Fr\"oberg's conjecture
Alexander Blomenhofer, Alex Casarotti
arxiv.org/abs/2509.10443

@arXiv_mathCT_bot@mastoxiv.page
2025-10-14 08:00:05

The smallest $n$-pure subtopos and dimension theory
Jens Hemelaer
arxiv.org/abs/2510.10349 arxiv.org/pdf/2510.10349