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Detectability of failures of linear programming (LP) decoding and the potential for improvement by adding new constraints motivate the use of an adaptive approach in selecting the constraints for the underlying LP problem. In this paper, we…

Information Theory · Computer Science 2007-07-13 Mohammad H. Taghavi , Paul H. Siegel

This article presents a general solution to the problem of computational complexity. First, it gives a historical introduction to the problem since the revival of the foundational problems of mathematics at the end of the 19th century.…

Computational Complexity · Computer Science 2023-12-25 Rami Zaidan

We address complexity issues for linear differential equations in characteristic $p>0$: resolution and computation of the $p$-curvature. For these tasks, our main focus is on algorithms whose complexity behaves well with respect to $p$. We…

Symbolic Computation · Computer Science 2009-01-27 Alin Bostan , Éric Schost

Discretization of the uniform norm of functions from a given finite dimensional subspace of continuous functions is studied. We pay special attention to the case of trigonometric polynomials with frequencies from an arbitrary finite set…

Numerical Analysis · Mathematics 2021-12-14 Boris Kashin , Sergei Konyagin , Vladimir Temlyakov

Computational complexity is examined using the principle of increasing entropy. To consider computation as a physical process from an initial instance to the final acceptance is motivated because many natural processes have been recognized…

Computational Complexity · Computer Science 2012-03-20 Arto Annila

For large classes of group testing problems, we derive lower bounds for the probability that all significant items are uniquely identified using specially constructed random designs. These bounds allow us to optimize parameters of the…

Statistics Theory · Mathematics 2022-02-17 Jack Noonan , Anatoly Zhigljavsky

The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples…

Machine Learning · Computer Science 2017-09-05 A. N. Gorban , I. Y. Tyukin

A numerical irreducible decomposition for a polynomial system provides representations for the irreducible factors of all positive dimensional solution sets of the system, separated from its isolated solutions. Homotopy continuation methods…

Mathematical Software · Computer Science 2018-06-19 Jan Verschelde

We consider the decidability of state-to-state reachability in linear time-invariant control systems over continuous time. We analyse this problem with respect to the allowable control sets, which are assumed to be the image under a linear…

Optimization and Control · Mathematics 2021-03-16 Mohan Dantam , Amaury Pouly

Research efforts of the past fifty years have led to a development of linear integer programming as a mature discipline of mathematical optimization. Such a level of maturity has not been reached when one considers nonlinear systems subject…

Optimization and Control · Mathematics 2017-01-03 Raymond Hemmecke , Matthias Köppe , Jon Lee , Robert Weismantel

We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires…

Machine Learning · Computer Science 2021-03-30 Ameesh Shah , Eric Zhan , Jennifer J. Sun , Abhinav Verma , Yisong Yue , Swarat Chaudhuri

We present an extension to the $\mathtt{mathlib}$ library of the Lean theorem prover formalizing the foundations of computability theory. We use primitive recursive functions and partial recursive functions as the main objects of study, and…

Logic in Computer Science · Computer Science 2019-07-19 Mario Carneiro

Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis? -- and which increasingly form the basis for life-altering…

Machine Learning · Computer Science 2020-11-30 Cynthia Dwork , Michael P. Kim , Omer Reingold , Guy N. Rothblum , Gal Yona

We consider the problem of efficiently computing the derivative of the solution map of a convex cone program, when it exists. We do this by implicitly differentiating the residual map for its homogeneous self-dual embedding, and solving the…

Optimization and Control · Mathematics 2020-05-21 Akshay Agrawal , Shane Barratt , Stephen Boyd , Enzo Busseti , Walaa M. Moursi

We consider the problem of distilling uniform random bits from an unknown source with a given $p$-entropy using linear hashing. As our main result, we estimate the expected $p$-divergence from the uniform distribution over the ensemble of…

Information Theory · Computer Science 2025-06-06 Madhura Pathegama , Alexander Barg

The classical decision problem, as it is understood today, is the quest for a delineation between the decidable and the undecidable parts of first-order logic based on elegant syntactic criteria. In this paper, we treat the concept of…

Logic in Computer Science · Computer Science 2019-11-27 Marco Voigt

In this work we present some results that allow to improve the decoding radius in solving polynomial linear systems with errors in the scenario where errors are additive and randomly distributed over a finite field. The decoding radius…

Information Theory · Computer Science 2020-03-05 Eleonora Guerrini , Romain Lebreton , Ilaria Zappatore

In the past decade, we had developed a series of splitting contraction algorithms for separable convex optimization problems, at the root of the alternating direction method of multipliers. Convergence of these algorithms was studied under…

Optimization and Control · Mathematics 2022-04-26 Bingsheng He , Xiaoming Yuan

One of my recent papers transforms an NP-Complete problem into the question of whether or not a feasible real solution exists to some Linear Program. The unique feature of this Linear Program is that though there is no explicit bound on the…

Computational Complexity · Computer Science 2010-03-08 Deepak Ponvel Chermakani

We generalize the feasible interpolation theorem for semantic derivations from K.(1997) by allowing randomized protocols (protocols in the sense of K.(1997). We also introduce an extension of the monotone circuit model, monotone circuits…

Logic · Mathematics 2018-11-26 Jan Krajicek