Global improvements of a protein alignment algorithm and comparison with a global optimization solver
Journal Title: Bulletin of Computational Applied Mathematics (Bull CompAMa) - Year 2013, Vol 1, Issue 1
Abstract
The LovoAlign method for Protein Alignment, based on the Low-Order Value Optimization theory, is recalled. The method is modified in order to improve global convergence properties and compared against other global minimization procedures.
Authors and Affiliations
Paulo Gouveia, Roberto Andreani, Ana Friedlander, José Mario Martínez, Leandro Martínez
Oceanic influence on extreme rainfall trends in the north central coast of Venezuela: present and future climate assessments
Extreme events are an important part of climate variability and their intensity and persistence are often modulated by large scale climatic patterns which might act as forcing drivers affecting their probability of occur...
Total dominator chromatic number of some operations on a graph
Let <i>G</i> be a simple graph. A total dominator coloring of <i>G</i> is a proper coloring of the vertices of <i>G</i> in which each vertex of the graph is adjacent to every vertex of some color class. The total dominat...
A globally convergent method for nonlinear least-squares problems based on the Gauss-Newton model with spectral correction
This work addresses a spectral correction for the Gauss-Newton model in the solution of nonlinear least-squares problems within a globally convergent algorithmic framework. The nonmonotone line search of Zhang and Hager...
Loop topological complexity
We introduce here the notion of loop motion planning algorithms and show that it yields to a homotopical invariant: the loop topological complexity, denoted throughout this paper by $\rm{TC}^{\rm{LP}}(-)$, which measures...
The exponential distribution as the sum of discontinuous distributions
We show that for any natural number $n$, an exponential distribution can be written as the sum of $n$ discontinuous variables and another exponential distribution, all of them independent.