The Kaczmarz method is an iterative algorithm for solving systems of linear equalities and inequalities, that iteratively projects onto these constraints. Recently, Strohmer and Vershynin [J. Fourier Anal. Appl., 15(2):262-278, 2009] gave a non-asymptotic convergence rate analysis for this algorithm, spurring numerous extensions and generalizations of the Kaczmarz method. Rather than the random...

Greedy Perimeter Stateless Routing (GPSR) is a Position-Based Routing Protocol for Wireless Networks that uses the Position of routers. GPSR makes greedy forwarding decision by using closest neighbor’s information of destination. In GPSR each node has the knowledge of its current physical position and also the neighbor node, this knowledge about node provides better routing. When a packet reach...

The Kaczmarz method is an iterative algorithm for solving systems of linear equalities and inequalities, that iteratively projects onto these constraints. Recently, Strohmer and Vershynin [J. Fourier Anal. Appl., 15(2):262-278, 2009] gave a non-asymptotic convergence rate analysis for this algorithm, spurring numerous extensions and generalizations of the Kaczmarz method. Rather than the random...

As a novel technology, the Internet of Things (IoT) has many applications in diverse fields, especially smart homes. IoT includes variety communication networks and technologies which facilitate between heterogeneous devices. One primary challenges is energy consumption. This paper introduces new Software Defined Network-based (SDN-based) clustering approach using intelligent algorithms for con...

Greedy algorithms exploit problem structure and constraints to achieve linear-time performance. Yet there is still no completely satisfactory way of constructing greedy algorithms. For example, the Greedy Algorithm of Edmonds depends upon translating a problem into an algebraic structure called a matroid, but the existence of such a translation can be as hard to determine as the existence of a ...

Probabilistic hybrid discrete/continuous models, such as Concurrent Probabilistic Hybrid Automata (CPHA) are convenient tools for modeling complex robotic systems. In this paper, we present a novel method for estimating the hybrid state of CPHA that achieves robustness by balancing greedy and stochastic search. To accomplish this, we 1) develop an efficient stochastic sampling approach for CPHA...

We propose a randomized greedy search algorithm to find point estimate for random partition based on loss function and posterior Monte Carlo samples. Given the large size awkward discrete nature of space, minimization expected is challenging. Our approach stochastic series optimizations performed in order embarrassingly parallel. consider several functions, including Binder variation informatio...

We consider the problem of reducing a potentially very large dataset to a subset of representative prototypes. Rather than searching over the entire space of prototypes, we first roughly divide the data into balanced clusters using bisecting k-means and spectral cuts, and then find the prototypes for each cluster by affinity propagation. We apply our algorithm to text data, where we perform an ...

illustrations, 75 numbered references. Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the ...

Recently, spectral clustering (a.k.a. normalized graph cut) techniques have become popular for their potential ability at finding irregularlyshaped clusters in data. The input to these methods is a similarity measure between every pair of data points. If the clusters are well-separated, the eigenvectors of the similarity matrix can be used to identify the clusters, essentially by identifying gr...