TY - GEN A1 - Koch, Thorsten A1 - Berthold, Timo A1 - Pedersen, Jaap A1 - Vanaret, Charlie T1 - Progress in Mathematical Programming Solvers from 2001 to 2020 N2 - This study investigates the progress made in LP and MILP solver performance during the last two decades by comparing the solver software from the beginning of the millennium with the codes available today. On average, we found out that for solving LP/MILP, computer hardware got about 20 times faster, and the algorithms improved by a factor of about nine for LP and around 50 for MILP, which gives a total speed-up of about 180 and 1,000 times, respectively. However, these numbers have a very high variance and they considerably underestimate the progress made on the algorithmic side: many problem instances can nowadays be solved within seconds, which the old codes are not able to solve within any reasonable time. T3 - ZIB-Report - 21-20 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-82779 SN - 1438-0064 ER - TY - JOUR A1 - Koch, Thorsten A1 - Berthold, Timo A1 - Pedersen, Jaap A1 - Vanaret, Charlie T1 - Progress in mathematical programming solvers from 2001 to 2020 JF - EURO Journal on Computational Optimization N2 - This study investigates the progress made in lp and milp solver performance during the last two decades by comparing the solver software from the beginning of the millennium with the codes available today. On average, we found out that for solving lp/milp, computer hardware got about 20 times faster, and the algorithms improved by a factor of about nine for lp and around 50 for milp, which gives a total speed-up of about 180 and 1,000 times, respectively. However, these numbers have a very high variance and they considerably underestimate the progress made on the algorithmic side: many problem instances can nowadays be solved within seconds, which the old codes are not able to solve within any reasonable time. Y1 - 2022 U6 - https://doi.org/10.1016/j.ejco.2022.100031 VL - 10 SP - 100031 ER - TY - GEN A1 - Cao, Karl-Kien A1 - Anderson, Lovis A1 - Böhme, Aileen A1 - Breuer, Thomas A1 - Buschmann, Jan A1 - Fiand, Frederick A1 - Frey, Ulrich A1 - Fuchs, Benjamin A1 - Kempe, Nils-Christian A1 - von Krbek, Kai A1 - Medjroubi, Wided A1 - Riehm, Judith A1 - Sasanpour, Shima A1 - Simon, Sonja A1 - Vanaret, Charlie A1 - Wetzel, Manuel A1 - Xiao, Mengzhu A1 - Zittel, Janina T1 - Evaluation of Uncertainties in Linear-Optimizing Energy System Models - Compendium T2 - DLR-Forschungsbericht N2 - Für die Energiesystemforschung sind Software-Modelle ein Kernelement zur Analyse von Szenarien. Das Forschungsprojekt UNSEEN hatte das Ziel eine bisher unerreichte Anzahl an modellbasierten Energieszenarien zu berechnen, um Unsicherheiten – vor allem unter Nutzung linear optimierender Energiesystem-Modelle - besser bewerten zu können. Hierfür wurden umfangreiche Parametervariationen auf Energieszenarien angewendet und das wesentliche methodische Hindernis in diesem Zusammenhang adressiert: die rechnerische Beherrschbarkeit der zu lösenden mathematischen Optimierungsprobleme. Im Vorläuferprojekt BEAM-ME wurde mit der Entwicklung und Anwendung des Open-Source-Lösers PIPS-IPM++ die Grundlage für den Einsatz von High-Performance-Computing (HPC) zur Lösung dieser Modelle gelegt. In UNSEEN war dieser Löser die zentrale Komponente eines Workflows, welcher zur Generierung, Lösung und multi-kriteriellen Bewertung von Energieszenarien auf dem Hochleistungscomputer JUWELS am Forschungszentrum Jülich implementiert wurde. Zur effizienten Generierung und Kommunikation von Modellinstanzen für Methoden der mathematischen Optimierung auf HPC wurde eine weitere Workflow-Komponente von der GAMS Software GmbH entwickelt: der Szenariogenerator. Bei der Weiterentwicklung von Lösungsalgorithmen für linear optimierende Energie-Systemmodelle standen gemischt-ganzzahlige Optimierungsprobleme im Fokus, welche für die Modellierung konkreter Infrastrukturen und Maßnahmen zur Umsetzung der Energiewende gelöst werden müssen. Die in diesem Zusammenhang stehenden Arbeiten zur Entwicklung von Algorithmen wurden von der Technischen Universität Berlin verantwortet. Bei Design und Implementierung dieser Methoden wurde sie vom Zuse Instituts Berlin unterstützt. Y1 - 2023 U6 - https://doi.org/10.57676/w2rq-bj85 IS - DLR-FB-2023-15 ER - TY - CHAP A1 - Kiessling, David A1 - Vanaret, Charlie A1 - Astudillo, Alejandro A1 - Decré, Wilm A1 - Swevers, Jan T1 - An Almost Feasible Sequential Linear Programming Algorithm T2 - European Control Conference 2024 N2 - This paper proposes an almost feasible Sequential Linear Programming (afSLP) algorithm. In the first part, the practical limitations of previously proposed Feasible Sequential Linear Programming (FSLP) methods are discussed along with illustrative examples. Then, we present a generalization of FSLP based on a tolerance-tube method that addresses the shortcomings of FSLP. The proposed algorithm afSLP consists of two phases. Phase I starts from random infeasible points and iterates towards a relaxation of the feasible set. Once the tolerance-tube around the feasible set is reached, phase II is started and all future iterates are kept within the tolerance-tube. The novel method includes enhancements to the originally proposed tolerance-tube method that are necessary for global convergence. afSLP is shown to outperform FSLP and the state-of-the-art solver IPOPT on a SCARA robot optimization problem. Y1 - 2024 ER - TY - GEN A1 - Vanaret, Charlie A1 - Leyffer, Sven T1 - Unifying nonlinearly constrained nonconvex optimization N2 - Derivative-based iterative methods for nonlinearly constrained non-convex optimization usually share common algorithmic components, such as strategies for computing a descent direction and mechanisms that promote global convergence. Based on this observation, we introduce an abstract framework based on four common ingredients that describes most derivative-based iterative methods and unifies their workflows. We then present Uno, a modular C++ solver that implements our abstract framework and allows the automatic generation of various strategy combinations with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno's software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTEst collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno . Y1 - 2024 ER - TY - JOUR A1 - Vanaret, Charlie T1 - Interval constraint programming for globally solving catalog-based categorical optimization JF - Journal of Global Optimization N2 - In this article, we propose an interval constraint programming method for globally solving catalog-based categorical optimization problems. It supports catalogs of arbitrary size and properties of arbitrary dimension, and does not require any modeling effort from the user. A novel catalog-based contractor (or filtering operator) guarantees consistency between the categorical properties and the existing catalog items. This results in an intuitive and generic approach that is exact, rigorous (robust to roundoff errors) and can be easily implemented in an off-the-shelf interval-based continuous solver that interleaves branching and constraint propagation. We demonstrate the validity of the approach on a numerical problem in which a categorical variable is described by a two-dimensional property space. A Julia prototype is available as open-source software under the MIT license. Y1 - 2024 U6 - https://doi.org/10.1007/s10898-023-01362-0 VL - 89 SP - 457 EP - 476 ER -