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We propose an algorithmic procedure i) to study the ``distance'' between an integrable PDE and any discretization of it, in the small lattice spacing epsilon regime, and, at the same time, ii) to test the (asymptotic) integrability…

Exactly Solvable and Integrable Systems · Physics 2015-05-13 Paolo Maria Santini

We introduce the concept of sos-convex Lyapunov functions for stability analysis of both linear and nonlinear difference inclusions (also known as discrete-time switched systems). These are polynomial Lyapunov functions that have an…

Optimization and Control · Mathematics 2018-03-07 Amir Ali Ahmadi , Raphael M. Jungers

The Ideal Membership Problem (IMP) tests if an input polynomial $f\in \mathbb{F}[x_1,\dots,x_n]$ with coefficients from a field $\mathbb{F}$ belongs to a given ideal $I \subseteq \mathbb{F}[x_1,\dots,x_n]$. It is a well-known fundamental…

Computational Complexity · Computer Science 2020-07-02 Arpitha P. Bharathi , Monaldo Mastrolilli

Depth separation results propose a possible theoretical explanation for the benefits of deep neural networks over shallower architectures, establishing that the former possess superior approximation capabilities. However, there are no known…

Machine Learning · Computer Science 2023-02-03 Itay Safran , Jason D. Lee

The separation of two sets (or more specific of two cones) plays an important role in different fields of mathematics such as variational analysis, convex analysis, convex geometry, optimization. In the paper, we derive some new results for…

Functional Analysis · Mathematics 2023-08-04 Christian Günther , Bahareh Khazayel , Christiane Tammer

We analyse how the standard reductions between constraint satisfaction problems affect their proof complexity. We show that, for the most studied propositional, algebraic, and semi-algebraic proof systems, the classical constructions of…

Computational Complexity · Computer Science 2018-09-26 Albert Atserias , Joanna Ochremiak

We study the existence of optimal and p-optimal proof systems for classes in the Boolean hierarchy over $\mathrm{NP}$. Our main results concern $\mathrm{DP}$, i.e., the second level of this hierarchy: If all sets in $\mathrm{DP}$ have…

Computational Complexity · Computer Science 2023-09-22 Fabian Egidy , Christian Glaßer , Martin Herold

In this note we show that unsatisfiable systems of linear equations with a constant number of variables per equation over prime finite fields have polynomial-size constant-degree semi-algebraic proofs of unsatisfiability. These are proofs…

Computational Complexity · Computer Science 2015-02-16 Albert Atserias

In this paper we study the fine-grained complexity of finding exact and approximate solutions to problems in P. Our main contribution is showing reductions from exact to approximate solution for a host of such problems. As one (notable)…

Computational Complexity · Computer Science 2022-12-12 Lijie Chen , Shafi Goldwasser , Kaifeng Lyu , Guy N. Rothblum , Aviad Rubinstein

Analysis of low-degree polynomial algorithms is a powerful, newly-popular method for predicting computational thresholds in hypothesis testing problems. One limitation of current techniques for this analysis is their restriction to…

Statistics Theory · Mathematics 2020-11-10 Dmitriy Kunisky

Proving that there are problems in $\mathsf{P}^\mathsf{NP}$ that require boolean circuits of super-linear size is a major frontier in complexity theory. While such lower bounds are known for larger complexity classes, existing results only…

Computational Complexity · Computer Science 2023-06-22 Jan Bydzovsky , Jan Krajicek , Igor C. Oliveira

Interactive proofs (IP) model a world where a verifier delegates computation to an untrustworthy prover, verifying the prover's claims before accepting them. IP protocols have applications in areas such as verifiable computation…

Computational Complexity · Computer Science 2017-11-15 Jing Chen , Samuel McCauley , Shikha Singh

Deep inference is a proof theoretic methodology that generalizes the standard notion of inference of the sequent calculus, whereby inference rules become applicable at any depth inside logical expressions. Deep inference provides more…

Logic in Computer Science · Computer Science 2015-07-01 Ozan Kahramanogullari

The Cholesky decomposition is a fundamental tool for solving linear systems with symmetric and positive definite matrices which are ubiquitous in linear algebra, optimization, and machine learning. Its numerical stability can be improved by…

Machine Learning · Computer Science 2025-07-29 Filip de Roos , Fabio Muratore

We propose a method called ideal regression for approximating an arbitrary system of polynomial equations by a system of a particular type. Using techniques from approximate computational algebraic geometry, we show how we can solve ideal…

The accumulated quadrature weights of Gaussian quadrature formulae constitute bounds on the integral over the intervals between the quadrature nodes. Classical results in this concern date back to works of Chebyshev, Markov and Stieltjes…

Numerical Analysis · Mathematics 2022-12-20 Tobias Jawecki

Within recent years, considerable progress has been made regarding high-performance solvers for Partial Differential Equations (PDEs), yielding potential gains in efficiency compared to industry standard tools. However, the latter largely…

Numerical Analysis · Mathematics 2024-02-20 Patrick Zimbrod , Michael Fleck , Johannes Schilp

An efficient entailment proof system is essential to compositional verification using separation logic. Unfortunately, existing decision procedures are either inexpressive or inefficient. For example, Smallfoot is an efficient procedure but…

Logic in Computer Science · Computer Science 2022-10-04 Quang Loc Le , Xuan-Bach D. Le

We present a stronger version of the Doherty-Parrilo-Spedalieri (DPS) hierarchy of approximations for the set of separable states. Unlike DPS, our hierarchy converges exactly at a finite number of rounds for any fixed input dimension. This…

Quantum Physics · Physics 2017-06-19 Aram W. Harrow , Anand Natarajan , Xiaodi Wu

Primal-dual interior-point methods solve constrained convex optimization problems to tight tolerances with speed and robustness. Their solutions are also efficiently differentiable with respect to the problem data through the implicit…

Optimization and Control · Mathematics 2026-05-19 Jon Arrizabalaga , Kevin Tracy , Zachary Manchester
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