Matroïde

Résumé

En mathématiques, et plus particulièrement en combinatoire, un matroïde est une structure introduite comme un cadre général pour le concept d'indépendance linéaire. Elle est donc naturellement liée à l'algèbre linéaire (déjà au niveau du vocabulaire : indépendant, base, rang), mais aussi à la théorie des graphes (circuit, cycle), à l'algorithmique (algorithme glouton), et à la géométrie (pour diverses questions liées à la représentation). La notion a été introduite en 1935 par Whitney. Le mot matroïde provient du mot matrice. Notion d'indépendance vignette|Trois vecteurs linéairement dépendants. L'indépendance linéaire d'une famille de vecteurs correspond au fait que l'on ne peut réécrire l'un des vecteurs comme combinaison linéaire des autres. En prenant l'exemple de la figure à droite, les vecteurs {a, b} forment un ensemble indépendant. Par contre, l'ensemble des vecteurs {a, b, c} n'est pas indépendants puisque c peut s'écrire comme c1a + c2b où c

Source officielle

https://fr.wikipedia.org/wiki/Matro%C3%AFde

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The most basic form of the max-sum dispersion problem (MSD) is as follows: given n points in R^q and an integer k, select a set of k points such that the sum of the pairwise distances within the set is maximal. This is a prominent diversity problem, with wide applications in web search and information retrieval, where one needs to find a small and diverse representative subset of a large dataset. The problem has recently received a great deal of attention in the computational geometry and operations research communities; and since it is NP-hard, research has focused on efficient heuristics and approximation algorithms. Several classes of distance functions have been considered in the literature. Many of the most common distances used in applications are induced by a norm in a real vector space. The focus of this thesis is on MSD over these geometric instances. We provide for it simple and fast polynomial-time approximation schemes (PTASs), as well as improved constant-factor approximation algorithms. We pay special attention to the class of negative-type distances, a class that includes Euclidean and Manhattan distances, among many others. In order to exploit the properties of this class, we apply several techniques and results from the theory of isometric embeddings. We explore the following variations of the MSD problem: matroid and matroid-intersection constraints, knapsack constraints, and the mixed-objective problem that maximizes a combination of the sum of pairwise distances with a submodular monotone function. In addition to approximation algorithms, we present a core-set for geometric instances of low dimension, and we discuss the efficient implementation of some of our algorithms for massive datasets, using the streaming and distributed models of computation.

EPFL2016

Chargement

Matching fields and lattice points of simplices

Georg Peter Loho

We show that the Chow covectors of a linkage matching field define a bijection of lattice points and we demonstrate how one can recover the linkage matching field from this bijection. This resolves two open questions from Sturmfels & Zelevinsky (1993) on linkage matching fields. For this, we give an explicit construction that associates a bipartite incidence graph of an ordered partition of a common set to all lattice points in a dilated simplex. Given a triangulation of a product of two simplices encoded by a set of bipartite trees, we similarly prove that the bijection from left to right degree vectors of the trees is enough to recover the triangulation. As additional results, we show a cryptomorphic description of linkage matching fields and characterise the flip graph of a linkage matching field in terms of its prodsimplicial flag complex. Finally, we relate our findings to transversal matroids through the tropical Stiefel map.

2018

Chargement

Streaming and Matching Problems with Submodular Functions

Paritosh Garg

Submodular functions are a widely studied topic in theoretical computer science. They have found several applications both theoretical and practical in the fields of economics, combinatorial optimization and machine learning. More recently, there have also been numerous works that study combinatorial problems with submodular objective functions. This is motivated by their natural diminishing returns property which is useful in real-world applications. The thesis at hand is concerned with the study of streaming and matching problems with submodular functions.Firstly, motivated by developing robust algorithms, we propose a new adversarial injections model, in which the input is ordered randomly, but an adversary may inject misleading elements at arbitrary positions. We study the maximum matching problem and cardinality constrained monotone submodular maximization. We show that even under this seemingly powerful adversary, it is possible to break the barrier of 1/2 for both these problems in the streaming setting. Our main result is a novel streaming algorithm that computes a 0.55-approximation for cardinality constrained monotone submodular maximization.In the second part of the thesis, we study the problem of matroid intersection in the semi-streaming setting. Our main result is a (2 + e)-approximate semi-streaming algorithm for weighted matroid inter- section improving upon the previous best guarantee of 4 + e. While our algorithm is based on the local ratio technique, its analysis differs from the related problem of weighted maximum matching and uses the concept of matroid kernels. We are also able to generalize our results to work for submodular functions by adapting ideas from a recent result by Levin and Wajc (SODA'21) on submodular maximization subject to matching constraints.Finally, we study the submodular Santa Claus problem in the restricted assignment case. The submodular Santa Claus problem was introduced in a seminal work by Goemans, Harvey, Iwata, and Mirrokni (SODA'09) as an application of their structural result. In the mentioned problem n unsplittable resources have to be assigned to m players, each with a monotone submodular utility function fi. The goal is to maximize mini fi(Si) where S1, . . . , Sm is a partition of the resources. The result by Goemans et al. implies a polynomial time O(n1/2+e)-approximation algorithm. In the restricted assignment case, each player is given a set of desired resources Gi and the individual valuation functions are defined as fi(S)= f(SnGi). OurmainresultisaO(loglog(n))-approximation algorithm for the problem. Our proof is inspired by the approach of Bansal and Srividenko (STOC'06) to the Santa Claus problem. Com- pared to the more basic linear setting, the introduction of submodularity requires a much more involved analysis and several new ideas.

EPFL2022

Chargement

Streaming and Matching Problems with Submodular Functions

Paritosh Garg

EPFL2022