Abstract. We consider a generalization of the well known greedy algorithm, called mstep greedy algorithm, where m elements are examined in each iteration. When m = 1 or 2, the algorithm reduces to the standard greedy algorithm. For m = 3 we provide a complete characterization of the independence system, called trioid, where the m-step greedy algorithm guarantees an optimal solution for all weig...

Perhaps the best known algorithm in combinatorial optimization is the greedy algorithm. A natural question is for which optimization problems does the greedy algorithm produce an optimal solution? In a sense this question is answered by a classical theorem in matroid theory due to Rado and Edmonds. In the matroid case, the greedy algorithm solves the optimization problem for every linear object...

This paper proposes a greedy algorithm named as Big step greedy set cover algorithm to compute approximate minimum set cover. The Big step greedy algorithm, in each step selects p sets such that the union of selected p sets contains greatest number of uncovered elements and adds the selected p sets to partial set cover. The process of adding p sets is repeated until all the elements are covered...

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the gen...

We consider the problem of studying the performance of greedy algorithm on sensor selection problem for stable linear systems with Kalman Filter. Specifically, the objective is to find the system parameters that affects the performance of greedy algorithms and conditions where greedy algorithm always produces optimal solutions. In this paper, we developed an upper bound for performance ratio of...

Greedy algorithm is a group of algorithms that have one common characteristic, making the best choice locally at each step without considering future plans. Thus, the essence of greedy algorithm is a choice function: given a set of options, choose the current best option. Because of the myopic nature of greedy algorithm, it is (as expected) not correct for many problems. However, there are cert...

Greedy algorithm is a group of algorithms that have one common characteristic, making the best choice locally at each step without considering future plans. Thus, the essence of greedy algorithm is a choice function: given a set of options, choose the current best option. Because of the myopic nature of greedy algorithm, it is (as expected) not correct for many problems. However, there are cert...

We present on O(n log n) greedy algorithm with a worst-case performance ratio > 4 for the unbounded knapsack problem, an O(n log n) greedy algorithm with a worst-case performance ratio of ~ for the subset-sum problem, and an O(n log n) greedy algorithm with a worst-case performance ratio of ~for the partition problem. These greedy algorithms, in the sense of worst-case performance, are better t...

Iterative approximation algorithms are successfully applied in parametric approximation tasks. In particular, reduced basis methods make use of the so called Greedy algorithm for approximating solution sets of parametrized partial differential equations. Recently, a-priori convergence rate statements for this algorithm have been given (Buffa et al 2009, Binev et al. 2010). The goal of the curre...