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2011
This first book on greedy approximation gives a systematic presentation of the fundamental results. It also contains an introduction to two hot topics in numerical mathematics: learning theory and compressed sensing. Nonlinear approximation is becoming increasingly important, especially since two types are frequently employed in applications: adaptive ...
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This first book on greedy approximation gives a systematic presentation of the fundamental results. It also contains an introduction to two hot topics in numerical mathematics: learning theory and compressed sensing. Nonlinear approximation is becoming increasingly important, especially since two types are frequently employed in applications: adaptive ...
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Greedy Approximation Algorithms
2013Greedy strategy is a simple and natural method in the design of approximation algorithms. This chapter presents greedy approximation algorithms for very broad classes of maximization problems and minimization problems and analyzes their approximation bounds.
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Approximation of Reset Thresholds with Greedy Algorithms
Fundamenta Informaticae, 2016The problem of approximate computation of reset thresholds of synchronizing automata has gained a lot of attention recently. We introduce a broad class of algorithms that compute reset words and analyze their approximation ratios. We present three series of automata that reveal inherent limitations of greedy strategies for approximation of reset ...
Ananichev, Dimitry S. +1 more
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Greedy in Approximation Algorithms
2006The objective of this paper is to characterize classes of problems for which a greedy algorithm finds solutions provably close to optimum. To that end, we introduce the notion of k-extendible systems, a natural generalization of matroids, and show that a greedy algorithm is a 1/k-factor approximation for these systems.
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Greedy approximation of characteristic functions
Proceedings of the Steklov Institute of Mathematics, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Greedy approximation algorithms for directed multicuts
Networks, 2005AbstractThe Directed Multicut (DM) problem is: given a simple directed graph G = (V, E) with positive capacities ue on the edges, and a set K ⊆ V × V of ordered pairs of nodes of G, find a minimum capacity K‐multicut; C ⊆ E is a K‐multicut if in G − C there is no (s, t)‐path for any (s, t) ⫅ K.
Kortsarts, Yana +2 more
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Greedy Kernel Approximation for Sparse Surrogate Modeling
2018Modern simulation scenarios frequently require multi-query or real-time responses of simulation models for statistical analysis, optimization, or process control. However, the underlying simulation models may be very time-consuming rendering the simulation task difficult or infeasible. This motivates the need for rapidly computable surrogate models. We
Haasdonk B., Santin G.
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Greedy Algorithm and m -Term Trigonometric Approximation
Constructive Approximation, 1998This paper is devoted to the nonlinear method of approximation of the following type. For the periodic function \(f\) it is taken as an approximant a trigonometric polynomial of the form \( G_m(f):= \sum_{k\in \Lambda} \widehat f (k) \exp{i(k,x)}\), where \(\Lambda\subset \mathbf Z^d\) is a set of cardinality \(m\) containing the indices of the \(m ...
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Convergence of regularized greedy approximations
Izvestiya: MathematicsWe consider a new version of a greedy algorithm in biorthogonal systems in separable Banach spaces. We consider approximations of an element $f$ via $m$-term greedy sum, which is constructed from the expansion by choosing the first $m$ greatest in absolute value coefficients.
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