Parallel stochastic estimation method of eigenvalue distribution

Parallel estimation of distribution algorithms

The reduction in the execution time is a factor that becomes very important when using many applications nowadays. In spite of the enhancement on computer hardware, this problem is always happening, as the increase in power is always related to the application of new methods that were also too time consuming in previous computer systems. Parallel programming techniques provide feasibility of so...

Parallel Estimation of Distribution Algorithms

The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion of a new formal description of EDA algorithm. This high level concept can be used to compare the ...

Parallel Efficiency of the Lanczos Method for Eigenvalue Problems

Two of the commonly used versions of the Lanczos method for eigenvalues problems are the shift-and-invert Lanczos method and the restarted Lanczos method. In this talk, we will address two questions, is the shift-and-invert Lanczos method a viable option on massively parallel machines and which one is more appropriate for a given eigenvalue problem?

Parallel Stochastic Estimation on Multicore Platforms

Some results on the symmetric doubly stochastic inverse eigenvalue problem

‎The symmetric doubly stochastic inverse eigenvalue problem (hereafter SDIEP) is to determine the necessary and sufficient conditions for an $n$-tuple $sigma=(1,lambda_{2},lambda_{3},ldots,lambda_{n})in mathbb{R}^{n}$ with $|lambda_{i}|leq 1,~i=1,2,ldots,n$‎, ‎to be the spectrum of an $ntimes n$ symmetric doubly stochastic matrix $A$‎. ‎If there exists an $ntimes n$ symmetric doubly stochastic ...