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Related papers: General monotonicity

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A monotonicity property of Harnack inequality is proved for positive invariant harmonic functions in the unit ball.

Classical Analysis and ODEs · Mathematics 2007-05-23 Yifei Pan , Mei Wang

In the paper, we study the convergence analysis of Tikhonov regularization in finding a zero of a maximal monotone operator using the notion of R-continuity. Applications to convex minimization and DC programming are provided.

Optimization and Control · Mathematics 2023-11-23 Ba Khiet Le

We analyze the notions of monotonicity and complete monotonicity for Markov Chains in continuous-time, taking values in a finite partially ordered set. Similarly to what happens in discrete-time, the two notions are not equivalent. However,…

Probability · Mathematics 2016-03-08 Paolo Dai Pra , Pierre-Yves Louis , Ida Minelli

This paper studies the problem of testing whether a function is monotone from a nonparametric Bayesian perspective. Two new families of tests are constructed. The first uses constrained smoothing splines, together with a hierarchical…

Methodology · Statistics 2014-06-03 James G. Scott , Thomas S. Shively , Stephen G. Walker

We consider generalized solutions of the Perona-Malik equation in dimension one, defined as all possible limits of solutions to the semi-discrete approximation in which derivatives with respect to the space variable are replaced by…

Analysis of PDEs · Mathematics 2023-04-11 Massimo Gobbino , Nicola Picenni

In this work, optimality conditions and classical results from duality theory are derived for continuous-time linear optimization problems with inequality constraints. The optimality conditions are given in the Karush-Kuhn-Tucker form. Weak…

Optimization and Control · Mathematics 2023-05-10 Valeriano Antunes de Oliveira

This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this class when a small…

Optimization and Control · Mathematics 2013-03-19 Mark M. Tobenkin , Ian R. Manchester , Alexandre Megretski

The paper considers an extension of factor analysis to moving average processes. The problem is formulated as a rank minimization of a suitable spectral density. It is shown that it can be adequately approximated via a trace norm convex…

Optimization and Control · Mathematics 2015-08-26 Mattia Zorzi , Rodolphe Sepulchre

We propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties. The motivating problem is optimization of hyperparameters of machine learning…

Machine Learning · Computer Science 2018-02-20 Wenyi Wang , William J. Welch

The article considers the generalized k-Bessel functions and represents it as Wright functions. Then we study the monotonicity properties of the ratio of two different orders k- Bessel functions, and the ratio of the k-Bessel and the…

Classical Analysis and ODEs · Mathematics 2017-02-21 Saiful R Mondal , Kottakkaran S. Nisar

Recent work of Brlek \textit{et al.} gives a characterization of digitally convex polyominoes using combinatorics on words. From this work, we derive a combinatorial symbolic description of digitally convex polyominoes and use it to analyze…

Discrete Mathematics · Computer Science 2013-06-11 Olivier Bodini , Alice Jacquot , Philippe Duchon , Ljuben R. Mutafchiev

It is well-known that assumptions of monotonicity in size-bias couplings may be used to prove simple, yet powerful, Poisson approximation results. Here we show how these assumptions may be relaxed, establishing explicit Poisson…

Probability · Mathematics 2019-01-30 Fraser Daly , Oliver Johnson

The convex hull $P_{n}$ of a Gaussian sample $X_{1},...,X_{n}$ in $R^{d}$ is a Gaussian polytope. We prove that the expected number of facets $E f_{d-1} (P_n)$ is monotonically increasing in $n$. Furthermore we prove this for random…

Probability · Mathematics 2017-06-27 Mareen Beermann , Matthias Reitzner

Flexible sparsity regularization means stably approximating sparse solutions of operator equations by using coefficient-dependent penalizations. We propose and analyse a general nonconvex approach in this respect, from both theoretical and…

Optimization and Control · Mathematics 2021-11-12 Daria Ghilli , Dirk A. Lorenz , Elena Resmerita

Variational inequalities (VIs) are a broad class of optimization problems encompassing machine learning problems ranging from standard convex minimization to more complex scenarios like min-max optimization and computing the equilibria of…

Machine Learning · Computer Science 2025-02-20 Eric Zhao , Tatjana Chavdarova , Michael Jordan

Approximations of optimization problems arise in computational procedures and sensitivity analysis. The resulting effect on solutions can be significant, with even small approximations of components of a problem translating into large…

Optimization and Control · Mathematics 2022-08-10 Johannes O. Royset

Approximations of functions with finite data often do not respect certain "structural" properties of the functions. For example, if a given function is non-negative, a polynomial approximation of the function is not necessarily also…

Numerical Analysis · Mathematics 2020-08-20 Vidhi Zala , Robert M. Kirby , Akil Narayan

Recently, Z. W. Sun put forward a series of conjectures on monotonicity of combinatorial sequences in the form of $\{z_n/z_{n-1}\}_{n=N}^\infty$ and $\{\sqrt[n+1]{z_{n+1}}/\sqrt[n]{z_n}\}_{n=N}^\infty$ for some positive integer $N$, where…

Combinatorics · Mathematics 2015-12-04 Brian Y. Sun

In this note we prove a condition of monotonicity for the integral functional $ F(g) = \int_a^b h(x)\, d[-g(x)] $ with respect to $g$, a function of bounded variation. This condition is applied to analyze the behavior of a generalized…

Classical Analysis and ODEs · Mathematics 2015-03-19 Stefano Bertoni

Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications. Traditionally, the study of submodular functions was based on binary function…

Machine Learning · Computer Science 2022-05-18 Loay Mualem , Moran Feldman