We consider a distributed compressed sensing scenario where multiple sensors observe correlated sparse signals and the sensors are connected through a network. The signal correlation is realized by a partial common support-set. For such a system, the main objective of this paper is to develop a greedy pursuit algorithm. To fulfill the objective, we develop distributed parallel pursuit algorithm...

We prove that the graph tautology principles of Alekhnovich, Johannsen, Pitassi and Urquhart have polynomial size pool resolution refutations that use only input lemmas as learned clauses and without degenerate resolution inferences. These graph tautology principles can be refuted by polynomial size DPLL proofs with clause learning, even when restricted to greedy, unit-propagating DPLL search.

In this paper we present several algorithmic techniques for inferring the structure of a company when only a limited amount of information is available. We consider problems with two types of inputs: the number of pairs of employees with a given property and restricted information about the hierarchical structure of the company. We provide dynamic programming and greedy algorithms for these pro...

Orthogonal matching pursuit (OMP) is a canonical greedy algorithm for sparse signal reconstruction. When the signal of interest is block sparse, i.e., it has nonzero coefficients occurring in clusters, the block version of OMP algorithm (i.e., Block OMP) outperforms the conventional OMP. In this paper, we demonstrate that a new notion of block restricted isometry property (Block RIP), which is ...

We introduce a tree-based method to find short Double-Base chains. As compared to the classical greedy approach, this new method is not only simpler to implement and faster, experimentally it also returns shorter chains on average. The complexity analysis shows that the average length of a chain returned by this tree-based approach is log2 n 4.6419 · This tends to suggest that the average lengt...

Boltzmann machines are often used as building blocks in greedy learning of deep networks. However, training even a simplified model, known as restricted Boltzmann machine (RBM), can be extremely laborious: Traditional learning algorithms often converge only with the right choice of the learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data repres...

We first explain the research problem of finding the sparse solution of underdetermined linear systems with some applications. Then we explain three different approaches how to solve the sparse solution: the l1 approach, the orthogonal greedy approach, and the lq approach with 0 < q ≤ 1. We mainly survey recent results and present some new or simplified proofs. In particular, we give a good rea...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

A development environment for applications specified in an extended version of a previously developed coordination model based on associatively broadcast interactions is presented. The previous associative broadcast coordination model is extended to incorporate more complex specifications for interactions including multiple message interactions and fault-tolerance by replication. The runtime sy...