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...

Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks; conditions where stochastic algorithms are often effective. However, there are many such algorithms and the best one to use is not obvious. Here we compare multiple variants of the genetic algorithm, hill climbing, simulated annea...

A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, solutions of underdetermined linear systems of equations. Many such algorithms have now been proven using the ubiquitous Restricted Isometry Property (RIP) [9] to have optimal-order uniform recovery guarantees. However,...

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...

The Particle Therapy Patient Scheduling Problem (PTPSP) arises in modern cancer treatment facilities that provide particle therapy and consists of scheduling a set of therapies within a planning horizon of several months. A particularity of PTPSP compared to classical radiotherapy scheduling is that therapies need not only be assigned to days but also scheduled within each day to account for th...

The iterated di erence of polyhedra V P n P n Pk has been proposed independently in and as a su cient condition for V to be exactly computable by a two layered neural network An algorithm checking whether V IR is an iterated di erence of polyhedra is proposed in However this algorithm is not practically usable because it has a high computational complexity and it was only conjectured to stop wi...