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Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we…

Machine Learning · Statistics 2018-06-27 Alessandro Rudi , Carlo Ciliberto , Gian Maria Marconi , Lorenzo Rosasco

We introduce an alternative approach for constrained mathematical programming problems. It rests on two main aspects: an efficient way to compute optimal solutions for unconstrained problems, and multipliers regarded as variables for a…

Optimization and Control · Mathematics 2015-10-27 Pablo Pedregal

A new numerical approach is proposed for the simulation of coupled three-dimensional and one-dimensional elliptic equations (3D-1D coupling) arising from dimensionality reduction of 3D-3D problems with thin inclusions. The method is based…

Numerical Analysis · Mathematics 2021-11-24 Stefano Berrone , Denise Grappein , Stefano Scialo'

Several ways to accelerate the solution of 2D/3D linear min-max problems in $n$ constraints are discussed. We also present an algorithm for solving such problems in the 2D case, which is superior to CGAL's linear programming solver, both in…

Numerical Analysis · Computer Science 2012-05-29 Carmi Grushko

Many important physical situations such as fluid flows, marine environment, solid-state physics and plasma physics have been represented by shallow water wave equation. In this article, we construct new solitary wave solutions for the…

Pattern Formation and Solitons · Physics 2018-08-22 Sachin Kumar , Dharmendra Kumar

A common belief in high-dimensional data analysis is that data are concentrated on a low-dimensional manifold. This motivates simultaneous dimension reduction and regression on manifolds. We provide an algorithm for learning gradients on…

Statistics Theory · Mathematics 2010-02-24 Sayan Mukherjee , Qiang Wu , Ding-Xuan Zhou

Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties…

Machine Learning · Statistics 2024-03-19 Wu Lin , Valentin Duruisseaux , Melvin Leok , Frank Nielsen , Mohammad Emtiyaz Khan , Mark Schmidt

This paper presents a new mixed finite element method for the Cahn-Hilliard equation. The well-posedness of the mixed formulation is established and the error estimates for its linearized fully discrete scheme are provided. The new mixed…

Numerical Analysis · Mathematics 2026-02-11 Zhen Liu , Rui Ma , Min Zhang

A new numerical method for solving a scalar ordinary differential equation with a given initial condition is introduced. The method is using a numerical integration procedure for an equivalent integral equation and is called in this paper…

Numerical Analysis · Mathematics 2011-09-06 Alexander Lozovskiy

This paper presents a methodology for finding numerically, by means of curve-following, all real solutions of a general system of $n$ nonlinear equations in $n$ unknowns, within a given $n$-dimensional box. The main idea behind our method…

Numerical Analysis · Mathematics 2026-03-17 Katerina G. Hadjifotinou

The goal of this paper is to provide computational tools able to find a solution of a system of polynomial inequalities. The set of inequalities is reformulated as a system of polynomial equations. Three different methods, two of which…

Dynamical Systems · Mathematics 2016-03-04 Laura Menini , Corrado Possieri , Antonio Tornambè

Cylindrical Algebraic Decomposition (CAD) is an important tool within computational real algebraic geometry, capable of solving many problems for polynomial systems over the reals. It has long been studied by the Symbolic Computation…

Symbolic Computation · Computer Science 2018-11-01 Alexander Imani Cowen-Rivers , Matthew England

The effectiveness of projection methods for solving systems of linear inequalities is investigated. It is shown that they have a computational advantage over some alternatives and that this makes them successful in real-world applications.…

Optimization and Control · Mathematics 2009-12-23 Y. Censor , W. Chen , P. L. Combettes , R. Davidi , G. T. Herman

In this article we present logarithmic methods for solving first order and second order ordinary differential equations. The essence of the method is that we apply the basic properties derivatives and logarithms to reduce the number of…

General Mathematics · Mathematics 2023-01-05 Artem Ponomarenko

The spectral numerical mode-matching (SNMM) method is developed to simulate the 3D layered multi-region structures. The SNMM method is a semi-analytical solver having the properties of dimensionality reduction to reduce computational costs;…

Optics · Physics 2019-07-10 Jie Liu , Na Liu , Qing Huo Liu

Maximum Variance Unfolding is one of the main methods for (nonlinear) dimensionality reduction. We study its large sample limit, providing specific rates of convergence under standard assumptions. We find that it is consistent when the…

Machine Learning · Statistics 2013-03-21 Ery Arias-Castro , Bruno Pelletier

For singular perturbation problems in dynamical systems, various appropriate singular perturbation methods have been proposed to eliminate secular terms appearing in the naive expansion. For example, the method of multiple time scales, the…

Chaotic Dynamics · Physics 2009-11-13 Masatomo Iwasa

A nonlinear transformation of the dispersive long wave equations in (2+1) dimensions is derived by using the homogeneous balance method. With the aid of the transformation given here, exact solutions of the equations are obtained.

Analysis of PDEs · Mathematics 2015-06-26 Mingliang Wang , Yubin Zhou , Zhibin Li

Consider the set of solutions to a system of polynomial equations in many variables. An algebraic manifold is an open submanifold of such a set. We introduce a new method for computing integrals and sampling from distributions on algebraic…

Algebraic Geometry · Mathematics 2020-03-10 Paul Breiding , Orlando Marigliano

Probabilistic Manifold Decomposition (PMD)\cite{doi:10.1137/25M1738863}, developed in our earlier work, provides a nonlinear model reduction by embedding high-dimensional dynamics onto low-dimensional probabilistic manifolds. The PMD has…

Numerical Analysis · Mathematics 2026-01-13 Jiaming Guo , Dunhui Xiao