Fonction linéaire (analyse)

Résumé

Dans les mathématiques élémentaires, les fonctions linéaires sont parmi les fonctions les plus simples que l'on rencontre. Ce sont des cas particuliers d'applications linéaires. Elles traduisent la proportionnalité. Par exemple, on dira que le prix d'un plein d'essence est fonction linéaire du nombre de litres mis dans le réservoir car :

pour zéro litre, on paie zéro euro ;
pour un litre, on paie 1,40 euro ;
pour 2 litres on paie 2,80 euros ;
pour 10 litres on paie 14 euros ;
pour 100 litres on paie 140 euros ;
et pour N litres, on paie 1,4 × N euros.

Remarque : Faux-ami avec le français, les termes allemands Lineare Funktion et anglais Linear function désignent une fonction affine. Comment reconnaître une fonction linéaire ? Une fonction linéaire est définie de la manière suivante : :f: x \mapsto y avec y = ax où le nombre a est un réel quelconque. Ce réel a s'appelle le coefficient de proportionnalité. En repartant de l'égalité y = ax, on v

Source officielle

https://fr.wikipedia.org/wiki/Fonction_lin%C3%A9aire_(analyse)

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Submodular Stochastic Probing on Matroids

Marek Adamczyk, Justin Dean Ward

In a stochastic probing problem we are given a universe E, and a probability that each element e in E is active. We determine if an element is active by probing it, and whenever a probed element is active, we must permanently include it in our solution. Moreover, throughout the process we need to obey inner constraints on the set of elements taken into the solution, and outer constraints on the set of all probed elements. All previous algorithmic results in this framework have considered only the problem of maximizing a linear function of the active elements. Here, we consider submodular objectives. We provide new, constant-factor approximations for maximizing a monotone submodular function subject to multiple matroid constraints on both the elements that may be taken and the elements that may be probed. We also obtain an improved approximation for linear objective functions, and show how our approach may be generalized to handle k-matchoid constraints.

INFORMS2016

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Subgraph counting is a fundamental primitive in graph processing, with applications in social network analysis (e.g., estimating the clustering coefficient of a graph), database processing and other areas. The space complexity of subgraph counting has been studied extensively in the literature, but many natural settings are still not well understood. In this paper we revisit the subgraph (and hypergraph) counting problem in the sketching model, where the algorithm's state as it processes a stream of updates to the graph is a linear function of the stream. This model has recently received a lot of attention in the literature, and has become a standard model for solving dynamic graph streaming problems. In this paper we give a tight bound on the sketching complexity of counting the number of occurrences of a small subgraph H in a bounded degree graph G presented as a stream of edge updates. Specifically, we show that the space complexity of the problem is governed by the fractional vertex cover number of the graph H. Our subgraph counting algorithm implements a natural vertex sampling approach, with sampling probabilities governed by the vertex cover of H. Our main technical contribution lies in a new set of Fourier analytic tools that we develop to analyze multiplayer communication protocols in the simultaneous communication model, allowing us to prove a tight lower bound. We believe that our techniques are likely to find applications in other settings. Besides giving tight bounds for all graphs H, both our algorithm and lower bounds extend to the hypergraph setting, albeit with some loss in space complexity.

IEEE COMPUTER SOC2018

Chargement

Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature

Justin Dean Ward

We design new approximation algorithms for the problems of optimizing submodular and supermodular functions subject to a single matroid constraint. Specifically, we consider the case in which we wish to maximize a monotone increasing submodular function or minimize a monotone decreasing supermodular function with a bounded total curvature c. Intuitively, the parameter c represents how nonlinear a function f is: when c = 0, f is linear, while for c = 1, f may be an arbitrary monotone increasing submodular function. For the case of submodular maximization with total curvature c, we obtain a (1 − c/e)-approximation—the first improvement over the greedy algorithm of of Conforti and Cornuéjols from 1984, which holds for a cardinality constraint, as well as a recent analogous result for an arbitrary matroid constraint. Our approach is based on modifications of the continuous greedy algorithm and nonoblivious local search, and allows us to approximately maximize the sum of a nonnegative, monotone increasing submodular function and a (possibly negative) linear function. We show how to reduce both submodular maximization and supermodular minimization to this general problem when the objective function has bounded total curvature. We prove that the approximation results we obtain are the best possible in the value oracle model, even in the case of a cardinality constraint. We define an extension of the notion of curvature to general monotone set functions and show a (1 − c)-approximation for maximization and a 1/(1 − c)-approximation for minimization cases. Finally, we give two concrete applications of our results in the settings of maximum entropy sampling, and the column-subset selection problem.

INFORMS2017

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