Range reduction using fixed points

National audienceThe efficiency of sBB depends on many parameters, among which the width of the variable ranges at each node. The fastest range reduction algorithm is called Feasibility-Based Bounds Tightening (FBBT) : it is an iterative procedure that propagates bounds up and down the expression trees [1] representing the constraints in (1), tightening them by using the constraint bounds (-?, 0]. Depending on the instance, and even limited to linear constraints only, FBBT may not converge finitely to its limit point. Tolerance-based termination criteria yield finite termination but, in general, in unbounded time (for every time bound, there is an instance exceeding it). So, although the FBBT is practically fast, its theoretical worst-case complexity status is far from satisfactory

La première preuve d'optimalité pour le cluster de Lennard-Jones à cinq atomes

National audienceLe potentiel de Lennard-Jones est un modèle relativement réaliste décrivant les interactions entre deux atomes au sein d'un gaz rare. Déterminer la configuration la plus stable d'un cluster à N atomes revient à trouver les positions relatives des atomes qui minimisent l'énergie potentielle globale ; ce potentiel joue un rôle important dans le cadre des agrégats atomiques et les nanotechnologies. Le problème de cluster est NP-difficile et ouvert pour N > 4, et n'a jamais été résolu par des méthodes globales fiables. Nous proposons de résoudre le problème de cluster à cinq atomes de manière optimale avec des méthodes d'intervalles qui garantissent un encadrement du minimum global, même en présence d'arrondis. Notre modèle spatial permet d'éliminer certaines symétries du problème et de calculer des minorants plus précis dans le branch and bound par intervalles. Nous montrons que la meilleure solution connue du problème à cinq atomes est optimale, fournissons la configuration spatiale correspondante et comparons notre solveur fiable aux solveurs BARON et Couenne. Alors que notre solution est numériquement certifiée avec une précision de 10 −9 , les solutions de BARON et Couenne sont entachées d'erreurs numériques

Recursive McCormick Linearization of Multilinear Programs

Linear programming (LP) relaxations are widely employed in exact solution methods for multilinear programs (MLP). One example is the family of Recursive McCormick Linearization (RML) strategies, where bilinear products are substituted for artificial variables, which deliver a relaxation of the original problem when introduced together with concave and convex envelopes. In this article, we introduce the first systematic approach for identifying RMLs, in which we focus on the identification of linear relaxation with a small number of artificial variables and with strong LP bounds. We present a novel mechanism for representing all the possible RMLs, which we use to design an exact mixed-integer programming (MIP) formulation for the identification of minimum-size RMLs; we show that this problem is NP-hard in general, whereas a special case is fixed-parameter tractable. Moreover, we explore structural properties of our formulation to derive an exact MIP model that identifies RMLs of a given size with the best possible relaxation bound is optimal. Our numerical results on a collection of benchmarks indicate that our algorithms outperform the RML strategy implemented in state-of-the-art global optimization solvers.Comment: 22 pages, 11 figures, Under Revie

Monitoring planning for urban drainage networks

Urban drainage network (UDN) monitoring is an important task whose planning can be related to various purposes, as for example contaminant detection and epidemiological studies. This paper proposes two different strategies for the identification of a monitoring system for UDNs. The optimal solution, in terms of location and number of sensors, is firstly addressed using a deterministic approach. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. Secondly, the position of devices is also investigated using a new strategy based on the complex network theory (CNT) tools. The comparison between the results achieved by both the strategies is finally presented with reference to a benchmark network

La première preuve d'optimalité pour le cluster de Lennard-Jones à cinq atomes

Le potentiel de Lennard-Jones est un modèle relativement réaliste décrivant les interactions entre deux atomes au sein d'un gaz rare. Déterminer la configuration la plus stable d'un cluster à N atomes revient à trouver les positions relatives des atomes qui minimisent l'énergie potentielle globale ; ce potentiel joue un rôle important dans le cadre des agrégats atomiques et les nanotechnologies. Le problème de cluster est NP-difficile et ouvert pour N > 4, et n'a jamais été résolu par des méthodes globales fiables. Nous proposons de résoudre le problème de cluster à cinq atomes de manière optimale avec des méthodes d'intervalles qui garantissent un encadrement du minimum global, même en présence d'arrondis. Notre modèle spatial permet d'éliminer certaines symétries du problème et de calculer des minorants plus précis dans le branch and bound par intervalles. Nous montrons que la meilleure solution connue du problème à cinq atomes est optimale, fournissons la configuration spatiale correspondante et comparons notre solveur fiable aux solveurs BARON et Couenne. Alors que notre solution est numériquement certifiée avec une précision de 10 −9 , les solutions de BARON et Couenne sont entachées d'erreurs numériques

Developing an Enhanced Algorithms to Solve Mixed Integer Non-Linear Programming Problems Based on a Feasible Neighborhood Search Strategy

Engineering optimization problems often involve nonlinear objective functions, which can capture complex relationships and dependencies between variables. This study focuses on a unique nonlinear mathematics programming problem characterized by a subset of variables that can only take discrete values and are linearly separable from the continuous variables. The combination of integer variables and non-linearities makes this problem much more complex than traditional nonlinear programming problems with only continuous variables. Furthermore, the presence of integer variables can result in a combinatorial explosion of potential solutions, significantly enlarging the search space and making it challenging to explore effectively. This issue becomes especially challenging for larger problems, leading to long computation times or even infeasibility. To address these challenges, we propose a method that employs the "active constraint" approach in conjunction with the release of nonbasic variables from their boundaries. This technique compels suitable non-integer fundamental variables to migrate to their neighboring integer positions. Additionally, we have researched selection criteria for choosing a nonbasic variable to use in the integerizing technique. Through implementation and testing on various problems, these techniques have proven to be successful

Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits

Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo

The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61 (Formula presented.) while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time