Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.

Coordinate descent optimization for l 1 minimization with application to compressed sensing; a greedy algorithm

Abstract: We propose a fast algorithm for solving the Basis Pursuit problem, minu $\{|u|_1\: \Au=f\}$, which has application to compressed sensing We design an efficient method for solving the related unconstrained problem minu $E(u) = |u|_1 + \lambda \||Au-f\||^2_2$ based on a greedy coordinate descent method We claim that in combination with a Bregman iterative method, our algorithm will achieve a solution with speed and accuracy competitive with some of the leading methods for the basis pursuit problem

Greedy feature selection for subspace clustering

Abstract: Unions of subspaces provide a powerful generalization of single subspace models for collections of high-dimensional data; however, learning multiple subspaces from data is challenging due to the fact that segmentation--the identification of points that live in the same subspace--and subspace estimation must be performed simultaneously. Recently, sparse recovery methods were shown to provide a provable and robust strategy for exact feature selection (EFS)--recovering subsets of points from the ensemble that live in the same subspace. In parallel with recent studies of EFS with l1-minimization, in this paper, we develop sufficient conditions for EFS with a greedy method for sparse signal recovery known as orthogonal matching pursuit (OMP). Following our analysis, we provide an empirical study of feature selection strategies for signals living on unions of subspaces and characterize the gap between sparse recovery methods and nearest neighbor (NN)-based approaches. In particular, we demonstrate that sparse recovery methods provide significant advantages over NN methods and that the gap between the two approaches is particularly pronounced when the sampling of subspaces in the data set is sparse. Our results suggest that OMP may be employed to reliably recover exact feature sets in a number of regimes where NN approaches fail to reveal the subspace membership of points in the ensemble.

NETAL: a new graph-based method for global alignment of protein–protein interaction networks

Abstract: Motivation: The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment. Moreover, time complexity of current algorithms makes them impossible to use for multiple alignment of several large networks together. Results: We present a novel algorithm for the global alignment of protein–protein interaction networks. It uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. NETAL outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, NETAL overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein–protein interaction networks. Availability: Binaries supported on linux are freely available for download at http://www.bioinf.cs.ipm.ir/software/netal. Contact: sh.arab@modares.ac.ir Supplementary information: Supplementary data are available at Bioinformatics online.

Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms

Abstract: In this study, a new mutation operator has been developed to increase Genetic Algorithm (GA) performance to find the shortest distance in the known Traveling Salesman Problem (TSP). We called this method as Greedy Sub Tour Mutation (GSTM). There exist two different greedy search methods and a component that provides a distortion in this new operator. The developed GSTM operator was tested with simple GA mutation operators in 14 different TSP examples selected from TSPLIB. The application of this GSTM operator gives much more effective results regarding to the best and average error values. The GSTM operator used with simple GAs decreases the best error values according to the other mutation operators with the ratio of between 74.24% and 88.32% and average error values between 59.42% and 79.51%.

Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

Abstract: We propose a cognitive radar network (CRN) system for the joint estimation of the target state comprising the positions and velocities of multiple targets, and the channel state comprising the propagation conditions of an urban transmission channel. We develop a measurement model for the received signal by considering a finite-dimensional representation of the time-varying system function which characterizes the urban transmission channel. We employ sequential Bayesian filtering at the receiver to estimate the target and the channel state. We propose a hybrid Bayesian filter that operates by partitioning the state space into smaller subspaces and thereby reducing the complexity involved with high-dimensional state space. The feedback loop that embodies the radar environment and the receiver enables the transmitter to employ approximate greedy programming to find a suitable subset of antennas to be employed in each tracking interval, as well as the power transmitted by these antennas. We compute the posterior Cramer-Rao bound (PCRB) on the estimates of the target state and the channel state and use it as an optimization criterion for the antenna selection and power allocation algorithms. We use several numerical examples to demonstrate the performance of the proposed system.