New error measures and methods for realizing protein graphs from distance data

The interval Distance Geometry Problem (iDGP) consists in finding a realization in $\mathbb{R}^K$ of a simple undirected graph $G=(V,E)$ with nonnegative intervals assigned to the edges in such a way that, for each edge, the Euclidean distance between the realization of the adjacent vertices is within the edge interval bounds. In this paper, we focus on the application to the conformation of proteins in space, which is a basic step in determining protein function: given interval estimations of some of the inter-atomic distances, find their shape. Among different families of methods for accomplishing this task, we look at mathematical programming based methods, which are well suited for dealing with intervals. The basic question we want to answer is: what is the best such method for the problem? The most meaningful error measure for evaluating solution quality is the coordinate root mean square deviation. We first introduce a new error measure which addresses a particular feature of protein backbones, i.e. many partial reflections also yield acceptable backbones. We then present a set of new and existing quadratic and semidefinite programming formulations of this problem, and a set of new and existing methods for solving these formulations. Finally, we perform a computational evaluation of all the feasible solver$+$formulation combinations according to new and existing error measures, finding that the best methodology is a new heuristic method based on multiplicative weights updates

Formulation space search for two-dimensional packing problems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The two-dimension packing problem is concerned with the arrangement of items without overlaps inside a container. In particular we have considered the case when the items are circular objects, some of the general examples that can be found in the industry are related with packing, storing and transportation of circular objects. Although there are several approaches we want to investigate the use of formulation space search. Formulation space search is a fairly recent method that provides an easy way to escape from local optima for non-linear problems allowing to achieve better results. Despite the fact that it has been implemented to solve the packing problem with identical circles, we present an improved implementation of the formulation space search that gives better results for the case of identical and non-identical circles, also considering that they are packed inside different shaped containers, for which we provide the needed modifications for an appropriate implementation. The containers considered are: the unit circle, the unit square, two rectangles with different dimension (length 5, width 1 and length 10 width 1), a right-isosceles triangle, a semicircle and a right-circular quadrant. Results from the tests conducted shown several improvements over the best previously known for the case of identical circles inside three different containers: a right-isosceles triangle, a semicircle and a circular quadrant. In order to extend the scope of the formulation space search approach we used it to solve mixed-integer non-linear problems, in particular those with zero-one variables. Our findings suggest that our implementation provides a competitive way to solve these kind of problems.This study was funded by the Mexican National Council for Science and Technology (CONACyT)

A hybrid heuristic for solving mixed integer nonlinear programming problems

Orientadores: Márcia Aparecida Gomes Ruggiero, Antonio Carlos MorettiTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: O objetivo neste trabalho é abordar problemas formulados como MINLP (Mixed Integer Nonlinear Programming). Propomos um método de resolução heurístico baseado em ideias de métodos do tipo Restauração Inexata combinado com a heurística denominada Feasibility Pump. Os métodos de Restauração Inexata foram propostos para resolução de problemas não lineares com variáveis contínuas. As iterações envolvem duas fases, Restauração (fase da viabilidade) e Otimalidade. A heurística Feasibility Pump foi proposta para obter soluções factíveis para problemas de otimização com variáveis inteiras, MILPs (Mixed Integer Linear Programming) e MINLPs. Neste trabalho adaptamos as duas fases dos métodos de Restauração Inexata ao contexto de problemas com variáveis inteiras, MINLP, buscando avanços na viabilidade (fase da Restauração) através da heurística Feasibility Pump. Na fase de otimalidade resolvemos dois subproblemas, no primeiro a condição de integralidade sobre as variáveis é relaxada e construímos um PNL (Problema de Programação Não Linear), no segundo as restrições não lineares são relaxadas e construímos um MILP. Um processo mestre coordena os subproblemas que são resolvidos em cada fase. O desempenho do algoritmo foi analisado e validado através da resolução de um conjunto clássico de problemasAbstract: The aim of this work is to address problems formulated as MINLP (Mixed Integer Nonlinear Programming). We propose a heuristic resolution method based on Inexact Restoration methods combined with the Feasibility Pump heuristic. The Inexact Restoration methods were proposed for solving nonlinear problems with continuous variables. These methods involve two phases, Restoration (viability phase) and Optimality. The Feasibility Pump heuristic was proposed to obtain feasible solutions for optimization problems with integer variables, MILPs (Mixed Integer Linear Programming) and MINLPs. In this work we adapt the two phases of the Inexact Restoration method in the context of problems with integer variables, MINLP, seeking advances in feasibility (Restoration phase) through the Feasibility Pump heuristic. In the optimality phase, two subproblems are solved, in the first the integrality constraints are relaxed and we construct a NLP (Nonlinear Programming), in the second the nonlinear constraints are relaxed and we construct a MILP. A master process coordinates the subproblems to be solved at each stage. The performance of the final algorithm was analised in a set of classical problemsDoutoradoMatematica AplicadaDoutora em Matemática Aplicada2013/21515-9FAPESPCAPE