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The theory part of this paper is sketched as follows. Based on column stochastic average matrix $T_n$ selected as a basic substitution matrix, the method of advanced successive difference substitution is established. Then, a set of…

Symbolic Computation · Computer Science 2010-04-05 Yong Yao

Iterative majorize-minimize (MM) (also called optimization transfer) algorithms solve challenging numerical optimization problems by solving a series of "easier" optimization problems that are constructed to guarantee monotonic descent of…

Computation · Statistics 2015-10-23 Madison G. McGaffin , Jeffrey A. Fessler

We present a first-order theorem proving framework for establishing the correctness of functional programs implementing sorting algorithms with recursive data structures. We formalize the semantics of recursive programs in many-sorted…

Logic in Computer Science · Computer Science 2024-03-07 Pamina Georgiou , Márton Hajdu , Laura Kovács

The main objective of this paper is to introduce an algorithm for solving fractional and classical differential equations based on a new generalized fractional power series. The algorithm relies on expanding the solution of an FDE or an ODE…

General Mathematics · Mathematics 2024-06-26 Youness Assebbane , Mohamed Echchehira , Mohamed Bouaouid , Mustapha Atraoui

We present an algorithm which allows to solve analytically linear systems of differential equations which factorize to first order. The solution is given in terms of iterated integrals over an alphabet where its structure is implied by the…

High Energy Physics - Phenomenology · Physics 2018-12-19 J. Ablinger , J. Blümlein , P. Marquard , N. Rana , C. Schneider

We introduce a sorting machine consisting of $k+1$ stacks in series: the first $k$ stacks can only contain elements in decreasing order from top to bottom, while the last one has the opposite restriction. This device generalizes \cite{SM},…

Data Structures and Algorithms · Computer Science 2019-10-10 Giulio Cerbai , Lapo Cioni , Luca Ferrari

Consider a polynomial $F$ in $m$ variables and a finite point ensemble $S=S_1 \times ... \times S_m$. When given the leading monomial of $F$ with respect to a lexicographic ordering we derive improved information on the possible number of…

Information Theory · Computer Science 2011-01-27 Olav Geil , Casper Thomsen

We give a new algorithm for merging sorted lists of monomials. Together with a projection technique we obtain a new complexity bound for the BM-algorithm.

Commutative Algebra · Mathematics 2010-10-20 Samuel Lundqvist

A fundamental task underlying many important optimization problems, from influence maximization to sensor placement to content recommendation, is to select the optimal group of $k$ items from a larger set. Submodularity has been very…

Data Structures and Algorithms · Computer Science 2022-03-02 Jon Kleinberg , Emily Ryu , Éva Tardos

The Majorization Principle is a fundamental statement governing the dynamics of information processing in optimal and efficient quantum algorithms. While quantum computation can be modeled to be reversible, due to the unitary evolution…

In this paper, we use a new partial order, called the f-majorization order. The new order includes as special cases the majorization , the reciprocal majorization and the p-larger orders. We provide a comprehensive account of the…

Statistics Theory · Mathematics 2017-04-13 Esmaeil Bashkar , Hamzeh Torabi , Ali Dolati , Felix Belzunce

We present two algorithms for computing what we call the absolute factorization of a difference operator. We also give an algorithm to solve third order difference equations in terms of second order equations, together with applications to…

Commutative Algebra · Mathematics 2024-06-12 Heba Bou KaedBey , Mark van Hoeij , Man Cheung Tsui

In this paper, we study a fundamental problem in submodular optimization, which is called sequential submodular maximization. Specifically, we aim to select and rank a group of $k$ items from a ground set $V$ such that the weighted…

Machine Learning · Computer Science 2023-12-13 Shaojie Tang , Jing Yuan

We give necessary and sufficient conditions for majorization of realrooted polynomials sharing a common interlacer by means of residues coming from fraction decomposition. We also introduce a motivated notion called strong majorization, and…

Classical Analysis and ODEs · Mathematics 2023-07-25 Aurelien Gribinski

Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite increasing interest in FMs, there exists to date no efficient…

Machine Learning · Statistics 2016-10-17 Mathieu Blondel , Akinori Fujino , Naonori Ueda , Masakazu Ishihata

Recently a majorization method for optimizing partition functions of log-linear models was proposed alongside a novel quadratic variational upper-bound. In the batch setting, it outperformed state-of-the-art first- and second-order…

Machine Learning · Computer Science 2013-09-24 Anna Choromanska , Tony Jebara

Majorization-minimization algorithms consist of successively minimizing a sequence of upper bounds of the objective function. These upper bounds are tight at the current estimate, and each iteration monotonically drives the objective…

Optimization and Control · Mathematics 2015-02-03 Julien Mairal

We consider a few modifications of the Big prime modular $\gcd$ algorithm for polynomials in $\Z[x]$. Our modifications are based on bounds of degrees of modular common divisors of polynomials, on estimates of the number of prime divisors…

Number Theory · Mathematics 2014-07-23 Vahagn H. Mikaelian

The size and complexity of software and hardware systems have significantly increased in the past years. As a result, it is harder to guarantee their correct behavior. One of the most successful methods for automated verification of…

Artificial Intelligence · Computer Science 2011-07-04 O. Grumberg , S. Livne , S. Markovitch

Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function. Because of its simplicity and its wide applicability, this principle has been very popular in statistics and in signal…

Machine Learning · Statistics 2013-09-11 Julien Mairal
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