A Categorical Approach to Finiteness Conditions
David Forsman
https://arxiv.org/abs/2509.10204 https://arxiv.org/pdf/2509.10204
Computable Bases
Vasco Brattka, Emmanuel Rauzy
https://arxiv.org/abs/2510.09850 https://arxiv.org/pdf/2510.09850 …
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
https://arxiv.org/abs/2510.10450
Uniformly-S-pseudo-projective modules
Mohammad adarbeh, Mohammad Saleh
https://arxiv.org/abs/2510.10170 https://arxiv.org/pdf/2510.10170
Hey, wait a minute: on at-issue sensitivity in Language Models
Sanghee J. Kim, Kanishka Misra
https://arxiv.org/abs/2510.12740 https://arxiv.org/pdf/2510.1…
Multi-Copy Security in Unclonable Cryptography
Alper \c{C}akan, Vipul Goyal, Fuyuki Kitagawa, Ryo Nishimaki, Takashi Yamakawa
https://arxiv.org/abs/2510.12626 https://
Verification of Sequential Convex Programming for Parametric Non-convex Optimization
Rajiv Sambharya, Nikolai Matni, George Pappas
https://arxiv.org/abs/2511.10622 https://arxiv.org/pdf/2511.10622 https://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
Division algebras of slice-Nash functions
Cinzia Bisi, Antonio Carbone
https://arxiv.org/abs/2510.09779 https://arxiv.org/pdf/2510.09779
On defectivity of joins, reducible secants and Fr\"oberg's conjecture
Alexander Blomenhofer, Alex Casarotti
https://arxiv.org/abs/2509.10443 https://
The smallest $n$-pure subtopos and dimension theory
Jens Hemelaer
https://arxiv.org/abs/2510.10349 https://arxiv.org/pdf/2510.10349