Results 1  10
of
113,859
A greedy algorithm for aligning DNA sequences
 J. COMPUT. BIOL
, 2000
"... For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an alignment that is guaranteed to be theoretically optimal. We introduce a new greedy a ..."
Abstract

Cited by 576 (16 self)
 Add to MetaCart
For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an alignment that is guaranteed to be theoretically optimal. We introduce a new greedy
Greedy Algorithm:
"... A dominating set of a graph G = (V, E) is a subset S ⊆ V of the nodes such that for all nodes v ∈ V, either v ∈ S or a neighbor u of v is in S. There are many distributed applications where computing a small dominating set of the network graph is important. It is wellknown that computing a dominati ..."
Abstract
 Add to MetaCart
dominating set of minimal size is NPhard. We therefore look for approximation algorithms, that is, algorithms which produce solutions which are optimal up to a certain factor. 10.1 Sequential Greedy Algorithm In order to understand the problem, we start with a very simple sequential algorithm. We start
Approximation and learning by greedy algorithms
 Ann. Statist
, 2008
"... We consider the problem of approximating a given element f from a Hilbert space H by means of greedy algorithms and the application of such procedures to the regression problem in statistical learning theory. We improve on the existing theory of convergence rates for both the orthogonal greedy algor ..."
Abstract

Cited by 52 (8 self)
 Add to MetaCart
We consider the problem of approximating a given element f from a Hilbert space H by means of greedy algorithms and the application of such procedures to the regression problem in statistical learning theory. We improve on the existing theory of convergence rates for both the orthogonal greedy
Vector Greedy Algorithms
"... Our objective is to study nonlinear approximation with regard to redundant systems. Redundancy on the one hand offers much promise for greater efficiency in terms of approximation rate, but on the other hand gives rise to highly nontrivial theoretical and practical problems. Greedy type approximati ..."
Abstract

Cited by 64 (11 self)
 Add to MetaCart
approximations proved to be convenient and efficient ways of constructing mterm approximants. We introduce and study vector greedy algorithms that are designed with aim of constructing mth greedy approximants simultaneously for a given finite number of elements. We prove convergence theorems and obtain some
Approximate weak greedy algorithms
 Advances in Computational Mathematics
, 2001
"... We present a generalization of V. Temlyakov’s weak greedy algorithm, and give a sufficient condition for norm convergence of the algorithm for an arbitrary dictionary in a Hilbert space. We provide two counterexamples to show that the condition cannot be relaxed for general dictionaries. For a clas ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
We present a generalization of V. Temlyakov’s weak greedy algorithm, and give a sufficient condition for norm convergence of the algorithm for an arbitrary dictionary in a Hilbert space. We provide two counterexamples to show that the condition cannot be relaxed for general dictionaries. For a
Evaluating the Reverse Greedy Algorithm
, 2004
"... This paper present two meta heuristics, reverse greedy and future aware greedy, which are variants of the greedy algorithm. Both are based on the observation that guessing the impact of future selections is useful for the current selection. While the greedy algorithm makes the best local selection g ..."
Abstract
 Add to MetaCart
This paper present two meta heuristics, reverse greedy and future aware greedy, which are variants of the greedy algorithm. Both are based on the observation that guessing the impact of future selections is useful for the current selection. While the greedy algorithm makes the best local selection
Algorithm, and a Relaxed Greedy Algorithm.
"... Estimates are given for the rate of approximation of a function by means of greedy algorithms. The estimates apply to approximation from an arbitrary dictionary of functions. Three greedy algorithms are discussed: the Pure Greedy Algorithm, an Orthogonal Greedy ..."
Abstract
 Add to MetaCart
Estimates are given for the rate of approximation of a function by means of greedy algorithms. The estimates apply to approximation from an arbitrary dictionary of functions. Three greedy algorithms are discussed: the Pure Greedy Algorithm, an Orthogonal Greedy
The Greedy Algorithm for the Symmetric TSP
"... We corrected proofs of two results on the greedy algorithm for the Symmetric TSP and answered a question in Gutin and Yeo, Oper. Res. Lett. 30 (2002), 97–99. ..."
Abstract
 Add to MetaCart
We corrected proofs of two results on the greedy algorithm for the Symmetric TSP and answered a question in Gutin and Yeo, Oper. Res. Lett. 30 (2002), 97–99.
When the greedy algorithm fails
"... We provide a characterization of the cases when the greedy algorithm may produce the unique worst possible solution for the problem of finding a minimum weight base in a uniform independence system when the weights are taken from a finite range. We apply this theorem to TSP and the minimum bisection ..."
Abstract

Cited by 12 (4 self)
 Add to MetaCart
We provide a characterization of the cases when the greedy algorithm may produce the unique worst possible solution for the problem of finding a minimum weight base in a uniform independence system when the weights are taken from a finite range. We apply this theorem to TSP and the minimum
Greedy algorithms and matroids
, 2007
"... My main reference was [1]. Greedy algorithms and matroids are nicely described in Chapter 16 of [1]; there is a much more mathematicallyoriented presentation in Chapter 1 of [2]. Spanning trees are discussed in depth in Chapter 23 of [1]; unionfind (and other algorithms for disjoint sets) are in C ..."
Abstract
 Add to MetaCart
My main reference was [1]. Greedy algorithms and matroids are nicely described in Chapter 16 of [1]; there is a much more mathematicallyoriented presentation in Chapter 1 of [2]. Spanning trees are discussed in depth in Chapter 23 of [1]; unionfind (and other algorithms for disjoint sets
Results 1  10
of
113,859