Using a conic bundle method to accelerate both phases of a quadratic convex reformulation

We present algorithm MIQCR-CB that is an advancement of method MIQCR~(Billionnet, Elloumi and Lambert, 2012). MIQCR is a method for solving mixed-integer quadratic programs and works in two phases: the first phase determines an equivalent quadratic formulation with a convex objective function by solving a semidefinite problem $(SDP)$, and, in the second phase, the equivalent formulation is solved by a standard solver. As the reformulation relies on the solution of a large-scale semidefinite program, it is not tractable by existing semidefinite solvers, already for medium sized problems. To surmount this difficulty, we present in MIQCR-CB a subgradient algorithm within a Lagrangian duality framework for solving $(SDP)$ that substantially speeds up the first phase. Moreover, this algorithm leads to a reformulated problem of smaller size than the one obtained by the original MIQCR method which results in a shorter time for solving the second phase. We present extensive computational results to show the efficiency of our algorithm

Puzzle Game about Connectivity and Biological Corridors

Biodiversity puzzle gameLandscape fragmentation prevents some species from moving as they should. This fragmentation, mainly due to urbanization, agriculture and forest exploitation, is a major cause of biodiversity loss. One of the options commonly envisaged to remedy this fragmentation - restore some connectivity - is the development of biological corridors. These corridors are natural spaces, usually linear. They allow species to move between different areas that are natural habitats for them.The aim of this game is to raise awareness among different audiences about issues related to biodiversity conservation, and more specifically the notion of landscape connectivity. The question is approached in a playful way: to determine, in a hypothetical landscape, the network of biological corridors, at the lowest cost, that would connect a fragmented set of natural habitats. The costs associated with sites that could be protected to form a corridor include monetary costs, ecological costs and social costs. This game also makes it possible to evoke human activities likely to develop in unprotected sites and have a negative impact on the preservation of biodiversity. It is a very simple game that cannot, of course, take into account all the complexity inherent in connectivity problems

Designing Protected Area Networks

There is a broad consensus in considering that the loss of biodiversity is accelerating which is due, for example, to the destruction of habitats, overexploitation of wild species and climate change. Many countries have pledged at various international conferences to take swift measures to halt this loss of biodiversity. Among these measures, the creation of protected areas – which also contribute to food and water security, the fight against climate change and people’ health and well-being – plays a decisive role, although it is not sufficient on its own. In this book, we review classic and original problems associated with the optimal design of a network of protected areas, focusing on the modelling and practical solution of these problems. We show how to approach these optimisation problems within a unified framework, that of mathematical programming, a branch of mathematics that focuses on finding good solutions to a problem from a huge number of possible solutions. We describe efficient and often innovative modellings of these problems. Several strategies are also proposed to take into account the inevitable uncertainty concerning the ecological benefits that can be expected from protected areas. These strategies are based on the classical notions of probability and robustness. This book aims to help all those, from students to decision-makers, who are confronted with the establishment of a network of protected areas to identify the most effective solutions, taking into account ecological objectives, various constraints and limited resources. In order to facilitate the reading of this book, most of the problems addressed and the approaches proposed to solve them are illustrated by fully processed examples, and an appendix presents in some detail the basic mathematical concepts related to its content

Solving a general mixed-integer quadratic problem through convex reformulation : a computational study

International audienceLet (QP) be a mixed integer quadratic program that consists of minimizing a qua-dratic function subject to linear constraints. In this paper, we present a convex reformulation of (QP), i.e. we reformulate (QP) into an equivalent program, with a convex objective function. Such a reformulation can be solved by a standard solver that uses a branch and bound algorithm. This reformulation, that we call MIQCR (Mixed Integer Quadratic Convex Reformulation), is the best one within a convex reformulation scheme, from the continuous relaxation point of view. It is based on the solution of an SDP relaxation of (QP). Computational experiences were carried out with instances of (QP) with one equality constraint. The results show that most of the considered instances, with up to 60 variables, can be solved within 1 hour of CPU time by a standard solver

Programmation mathématique en tomographie discrète

La tomographie est un ensemble de techniques visant à reconstruirel intérieur d un objet sans toucher l objet lui même comme dans le casd un scanner. Les principes théoriques de la tomographie ont été énoncéspar Radon en 1917. On peut assimiler l objet à reconstruire à une image,matrice, etc.Le problème de reconstruction tomographique consiste à estimer l objet àpartir d un ensemble de projections obtenues par mesures expérimentalesautour de l objet à reconstruire. La tomographie discrète étudie le cas où lenombre de projections est limité et l objet est défini de façon discrète. Leschamps d applications de la tomographie discrète sont nombreux et variés.Citons par exemple les applications de type non destructif comme l imageriemédicale. Il existe d autres applications de la tomographie discrète, commeles problèmes d emplois du temps.La tomographie discrète peut être considérée comme un problème d optimisationcombinatoire car le domaine de reconstruction est discret et le nombrede projections est fini. La programmation mathématique en nombres entiersconstitue un outil pour traiter les problèmes d optimisation combinatoire.L objectif de cette thèse est d étudier et d utiliser les techniques d optimisationcombinatoire pour résoudre les problèmes de tomographie.The tomographic imaging problem deals with reconstructing an objectfrom a data called a projections and collected by illuminating the objectfrom many different directions. A projection means the information derivedfrom the transmitted energies, when an object is illuminated from a particularangle. The solution to the problem of how to reconstruct an object fromits projections dates to 1917 by Radon. The tomographic reconstructingis applicable in many interesting contexts such as nondestructive testing,image processing, electron microscopy, data security, industrial tomographyand material sciences.Discete tomography (DT) deals with the reconstruction of discret objectfrom limited number of projections. The projections are the sums along fewangles of the object to be reconstruct. One of the main problems in DTis the reconstruction of binary matrices from two projections. In general,the reconstruction of binary matrices from a small number of projections isundetermined and the number of solutions can be very large. Moreover, theprojections data and the prior knowledge about the object to reconstructare not sufficient to determine a unique solution. So DT is usually reducedto an optimization problem to select the best solution in a certain sense.In this thesis, we deal with the tomographic reconstruction of binaryand colored images. In particular, research objectives are to derive thecombinatorial optimization techniques in discrete tomography problems.PARIS-CNAM (751032301) / SudocSudocFranceF

Solution of the Generalized Noah's Ark Problem

The phylogenetic diversity (PD) of a set of species is a measure of the evolutionary distance among the species in the collection, based on a phylogenetic tree. Such a tree is composed of a root, of internal nodes and of leaves that correspond to the set of taxa under study. With each edge of the tree is associated a non-negative branch length (evolutionary distance). If a particular survival probability is associated with each taxon, the PD measure becomes the expected PD measure. In the Noah's Ark Problem (NAP) introduced by Weitzman (1998), these survival probabilities can be increased at some cost. The problem is to determine how best to allocate a limited amount of resources to maximize the expected PD of the considered species. It is easy to formulate the NAP as a (difficult) nonlinear 0-1 programming problem. The aim of this article is to show that a general version of the NAP (GNAP) can be solved simply and efficiently with any set of edge weights and any set of survival probabilities by using standard mixed-integer linear programming software. The crucial point to move from a nonlinear program in binary variables to a mixed-integer linear program, is to approximate the logarithmic function by the lower envelope of a set of tangents to the curve. Solving the obtained mixed-integer linear program provides not only a near-optimal solution but also an upper bound on the value of the optimal solution. We also applied this approach to a generalization of the Nature Reserve Problem (GNRP) that consists of selecting a set of regions to be conserved so that the expected phylogenetic diversity of the set of species present in these regions is maximized. In this case, the survival probabilities of different taxa are not independent of each other. Computational results are presented to illustrate potentialities of the approach. Near-optimal solutions with hypothetical phylogenetic trees comprising about 4,000 taxa are obtained in a few seconds or minutes of computing time for the GNAP, and in about 30 minutes for the GNRP. In all the cases the average guarantee varies from 0% to 1.20%