On the relationship between bilevel decomposition algorithms and direct interior-point methods

Engineers have been using bilevel decomposition algorithms to solve certain nonconvex large-scale optimization problems arising in engineering design projects. These algorithms transform the large-scale problem into a bilevel program with one upperlevel problem (the master problem) and several lower-level problems (the subproblems). Unfortunately, there is analytical and numerical evidence that some of these commonly used bilevel decomposition algorithms may fail to converge even when the starting point is very close to the minimizer. In this paper, we establish a relationship between a particular bilevel decomposition algorithm, which only performs one iteration of an interior-point method when solving the subproblems, and a direct interior-point method, which solves the problem in its original (integrated) form. Using this relationship, we formally prove that the bilevel decomposition algorithm converges locally at a superlinear rate. The relevance of our analysis is that it bridges the gap between the incipient local convergence theory of bilevel decomposition algorithms and the mature theory of direct interior-point methods

On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach

© 2023 The Authors. Published by Elsevier B.VA novel approach based on a specialized interior-point method (IPM) is presented for solving largescale stochastic multistage continuous optimization problems, which represent the uncertainty in strategic multistage and operational two-stage scenario trees. This new solution approach considers a splitvariable formulation of the strategic and operational structures. The specialized IPM solves the normal equations by combining Cholesky factorizations with preconditioned conjugate gradients, doing so for, respectively, the constraints of the stochastic formulation and those that equate the split-variables. We show that, for multistage stochastic problems, the preconditioner (i) is a block-diagonal matrix composed of as many shifted tridiagonal matrices as the number of nested strategic-operational two-stage trees, thus allowing the efficient solution of systems of equations; (ii) its complexity in a multistage stochastic problem is equivalent to that of a very large-scale two-stage problem. A broad computational experience is reported for large multistage stochastic supply network design (SND) and revenue management (RM) problems. Some of the most difficult instances of SND had 5 stages, 839 million linear variables, 13 million quadratic variables, 21 million constraints, and 3750 scenario tree nodes; while those of RM had 8 stages, 278 million linear variables, 100 million constraints, and 100,000 scenario tree nodes. For those problems, the proposed approach obtained the solution in 1.1 days using 174 gigabytes of memory for SND, and in 1.7 days using 83 gigabytes for RM; while CPLEX v20.1 required more than 53 days and 531 gigabytes for SND, and more than 19 days and 410 gigabytes for RM.J. Castro was supported by the MCIN/AEI/FEDER grant RTI2018-097580-B-I00. L.E. Escudero was supported by the MCIN/AEI/10.13039/501100011033 grant PID2021-122640OB-I00. J.F. Monge was supported by the MCIN/AEI/10.13039/501100011033/ERDF grants PID2019-105952GB-I00 and PID2021-122344NB-I00, and by PROMETEO/2021/063 grant funded by the government of the Valencia Community, Spain.Peer ReviewedPostprint (published version

Minimization of Collateral Damage in Airdrops and Airstrikes

Collateral damage presents a significant risk during air drops and airstrikes, risking citizens\u27 lives and property, straining the relationship between the United States Air Force and host nations. This dissertation presents a methodology to determine the optimal location for making supply airdrops in order to minimize collateral damage while maintaining a high likelihood of successful recovery. A series of non-linear optimization algorithms is presented along with their relative success in finding the optimal location in the airdrop problem. Additionally, we present a quick algorithm for accurately creating the Pareto frontier in the multi-objective airstrike problem. We demonstrate the effect of differing guidelines, damage functions, and weapon employment selection which significantly alter the location of the optimal aimpoint in this targeting problem. Finally, we have provided a framework for making policy decisions in fast-moving troops-in-contact situations where observers are unsure of the nature of possible enemy forces in both finite horizon and infinite horizon problems. Through the recursive technique of solving this Markov decision process we have demonstrated the effect of improved intelligence and differing weights for waiting and incorrect decisions in the face of uncertain situations

Optimisation stochastique des systèmes multi-réservoirs par l'agrégation de scénarios et la programmation dynamique approximative

Les problèmes de gestion des réservoirs sont stochastiques principalement à cause de l’incertitude sur les apports naturels. Ceci entraine des modèles d’optimisation de grande taille pouvant être difficilement traitables numériquement. La première partie de cette thèse réexamine la méthode d’agrégation de scénarios proposée par Rockafellar et Wets (1991). L’objectif consiste à améliorer la vitesse de convergence de l’algorithme du progressive hedgging sur lequel repose la méthode. L’approche traditionnelle consiste à utiliser une valeur fixe pour ce paramètre ou à l’ajuster selon une trajectoire choisie a priori : croissante ou décroissante. Une approche dynamique est proposée pour mettre à jour le paramètre en fonction d’information sur la convergence globale fournie par les solutions à chaque itération. Il s’agit donc d’une approche a posteriori. La thèse aborde aussi la gestion des réservoirs par la programmation dynamique stochastique. Celle-ci se prête bien à ces problèmes de gestion à cause de la nature séquentielle de leurs décisions opérationnelles. Cependant, les applications sont limitées à un nombre restreint de réservoirs. La complexité du problème peut augmenter exponentiellement avec le nombre de variables d’état, particulièrement quand l’approche classique est utilisée, i.e. en discrétisant l’espace des états de « manière uniforme ». La thèse propose une approche d’approximation sur une grille irrégulière basée sur une décomposition simpliciale de l’espace des états. La fonction de valeur est évaluée aux sommets de ces simplexes et interpolée ailleurs. À l’aide de bornes sur la vraie fonction, la grille est raffinée tout en contrôlant l’erreur d’approximation commise. En outre, dans un contexte décision-information spécifique, une hypothèse « uni-bassin », souvent utilisée par les hydrologues, est exploitée pour développer des formes analytiques pour l’espérance de la fonction de valeur. Bien que la méthode proposée ne résolve pas le problème de complexité non polynomiale de la programmation dynamique, les résultats d’une étude de cas industrielle montrent qu’il n’est pas forcément nécessaire d’utiliser une grille très dense pour approximer la fonction de valeur avec une précision acceptable. Une bonne approximation pourrait être obtenue en évaluant cette fonction uniquement en quelques points de grille choisis adéquatement.Reservoir operation problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large optimization models that may be difficult to handle numerically. The first part of this thesis revisits the scenario aggregation method proposed by Rochafellar and Wets (1991). Our objective is to improve the convergence of the progressive hedging algorithm on which the method is based. This algorithm is based on an augmented Lagrangian with a penalty parameter that plays an important role in its convergence. The classical approach consists in using a fixed value for the parameter or in adjusting it according a trajectory chosen a priori: decreasing or increasing. This thesis presents a dynamic approach to update the parameter based on information on the global convergence provided by the solutions at each iteration. Therefore, it is an a posteriori scheme. The thesis also addresses reservoir problems via stochastic dynamic programming. This scheme is widely used for such problems because of the sequential nature of the operational decisions of reservoir management. However, dynamic programing is limited to a small number of reservoirs. The complexity may increase exponentially with the dimension of the state variables, especially when the classical approach is used, i.e. by discretizing the state space into a "regular grid". This thesis proposes an approximation scheme over an irregular grid based on simplicial decomposition of the state space. The value function is evaluated over the vertices of these simplices and interpolated elsewhere. Using bounds on the true function, the grid is refined while controlling the approximation error. Furthermore, in a specific information-decision context, a "uni-bassin" assumption often used by hydrologists is exploited to develop analytical forms for the expectation of the value function. Though the proposed method does not eliminate the non-polynomial complexity of dynamic programming, the results of an industrial case study show that it is not absolutely necessary to use a very dense grid to appropriately approximate the value function. Good approximation may be obtained by evaluating this function at few appropriately selected grid points