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A scalable parallel interior point algorithm for stochastic linear programming and robust optimization

COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 143-158页

作者： Yang, DF Zenios, SA UNIV CYPRUS DEPT PUBL & BUSINESS ADM NICOSIA CYPRUS

We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear programming and robust optimization. A matrix factorization procedure is employed that exploits the structure of the constraint matrix, and it is implemented on parallel computers. The implementation is perfectly scalable. Extensive computational results are reported for a library of standard test problems from stochastic linear programming, and also for robust optimization formulations. The results show that the codes are efficient and stable for problems with thousands of scenarios. Test problems with 130 thousand scenarios, and a deterministic equivalent linear programming formulation with 2.6 million constraints and 18.2 million variables, are solved successfully.

关键词： planning under uncertainty parallel computing optimization software

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performance Comparisons of Socially Inspired Metaheuristic algorithms on Unconstrained Global optimization

Performance Comparisons of Socially Inspired Metaheuristic A...

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International conference on Computer, Communication and Computational Sciences (IC4S)

作者： Altay, Elif Varol Alatas, Bilal Firat Univ Dept Software Engn Elazig Turkey

ISBN: (纸本)9789811303418;9789811303401

In recent years, many efficient metaheuristic algorithms have been proposed for complex, multimodal, high-dimensional, and nonlinear search and optimization problems. Physical, chemical, or biological laws and rules have been utilized as source of inspiration for these algorithms. Studies on social behaviors of humans in recent years have shown that social processes, concepts, rules, and events can be considered and modeled as novel efficient metaheuristic algorithm. These novel and interesting socially inspired algorithms have shown to be more effective and robust than existing classical and metaheuristic algorithms in a large number of applications. In this work, performance comparisons of social-based optimization algorithms, namely brainstorm optimization algorithm, cultural algorithm, duelist algorithm, imperialist competitive algorithm, and teaching learning based optimization algorithms have been demonstrated within unconstrained global optimization problems for the first time. These algorithms are relatively interesting and popular, and many versions of them seem to be efficiently used within many different complex search and optimization problems.

关键词： optimization Metaheuristics Social-based algorithms

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LMA: A generic and efficient implementation of the Levenberg-Marquardt Algorithm

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software-PRACTICE & EXPERIENCE 2017年第11期47卷 1707-1727页

作者： Ramadasan, Datta Chevaldonne, Marc Chateau, Thierry UBP Inst Pascal UMR 6602 CNRSIFMA Campus Cezeaux4 Ave Blaise Pascal F-63171 Aubiere France

This paper presents an open-source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg-Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state-of-the-art best alternatives through extensive benchmarks on different kind of problems. Copyright (C) 2017 John Wiley & Sons, Ltd.

关键词： Levenberg-Marquardt nonlinear least squares C plus genericity meta-programming

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Steady-state optimization and nonlinear Model-predictive Control of a Reactive Distillation Process using the software Platform do-mpc

Steady-state Optimization and Nonlinear Model-predictive Con...

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IEEE conference on Control Applications (CCA)

作者： Tatulea-Codrean, A. Hasskerl, D. Urselmann, M. Engell, S. TU Dortmund Proc Dynam & Operat Grp Biochem & Chem Engn Dept Dortmund Germany

ISBN: (纸本)9781509007554

The increase in the computational power and the development of new efficient algorithms for numerical simulation and dynamic optimization have brought the model-based control of complex industrial chemical processes on the basis of nonlinear large-scale mathematical models within reach. One of the most challenging applications is the control of reactive distillation (RD) processes, due to the high complexity and nonlinearity that results from the tight integration of separation and chemical reactions in one apparatus and the presence of multiple steady states. Model-predictive control of such processes was investigated and shown to improve process performance in several theoretical studies, e.g. [1]. However, the reliable solution of the resulting dynamic optimization problems for such large-scale DAE models in real-time remains a challenge. In this paper we present the realization of nonlinear model predictive control (NMPC) for a RD process described by large DAE models of different levels of detail. The implementation is done by means of the software tool do-mpc [2], which is a development platform for the efficient implementation of dynamic optimal control problems. A two layer control approach is tested, where an evolutionary algorithm is used to determine optimal steady-state points based on a detailed model of the process. Tracking these points with the NMPC in a smooth and real-time feasible fashion is achieved by a second layer. The focus of the paper is on the parametrization and the performance of the numerical solutions of the dynamic optimization problem.

关键词： nonlinear MPC Reactive Distillation do-mpc IPOPT Automatic Differentiation CasADi

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A Greener Meta-Heuristics Scheduling Algorithm With Energized optimization Dynamics by Deeper Intelligence Fusion

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IEEE ACCESS 2021年 9卷 18373-18382页

作者： Wang, Jinglian Gong, Bin Liu, Hong Li, Shaohui Ludong Univ Dept Informat & Elect Engn Yantai 264025 Peoples R China Shandong Univ Dept Software Jinan 250101 Peoples R China Shandong Normal Univ Dept Informat Sci & Engn Jinan 250014 Peoples R China Shandong Key Lab Distributed Comp Software New Te Jinan 250014 Peoples R China

As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CA(R)_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CA(R)_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances.

关键词： Scheduling Dynamic scheduling Heuristic algorithms Measurement optimization Green products Program processors Greener energized optimization dynamics fusion intelligence meta-heuristics algorithm nonlinear heterogeneous scheduling

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A unified framework for the application and evaluation of different methods for neural parameter optimization

A unified framework for the application and evaluation of di...

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International Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mohacsi, Mate Torok, Mark Patrik Saray, Sara Kali, Szabolcs Pazmany Peter Catholic Univ Inst Expt Med Fac Informat Technol & Bion Budapest Hungary Inst Expt Med Budapest Hungary

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

Automated parameter search has become a standard method in the modeling of neural systems. These studies could potentially take advantage of recent developments in nonlinear optimization, and the availability of software packages containing high-quality implementations of algorithms that proved useful in other domains. However, a systematic comparison of the available algorithms for problems that are typical in neuroscience has not been performed. We developed a software tool for fitting the parameters of neural models, which provides intuitive, uniform access to a variety of state-of-the-art optimization algorithms implemented by four different Python packages. We also established a set of benchmark problems of different complexity that involve a variety of widely used neuronal models. We then used our optimization tool to systematically evaluate the performance of the algorithms on our set of benchmark problems. We found that several evolutionary and related algorithms consistently provided good solutions for all of our benchmarks. However, the relative performance of the different methods, both in terms of the quality of the final result and in terms of convergence speed, depended substantially on the nature of the problem. We hope that our software tool and benchmarking results will facilitate the choice and application of the best parameter-fitting methods in neuroscientific research.

关键词： neuronal modeling Python software simulation model fitting parameter optimization algorithm

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Energy Management in Plug-In Hybrid Electric Vehicles: Convex optimization algorithms for Model Predictive Control

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第6期28卷 2191-2203页

作者： East, Sebastian Cannon, Mark Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm and a general purpose convex optimization software CVX. It is found that the ADMM algorithm has favorable properties when a solution with modest accuracy is required, whereas the projected interior-point method is favorable when high accuracy is required, and that both are significantly faster than CVX.

关键词： Engines Energy management optimization Heuristic algorithms Batteries Convex functions Mechanical power transmission Alternating direction method of multipliers (ADMM) energy management interior-point method model predictive control (MPC) plug-in hybrid electric vehicles (PHEVs)

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Optimized start-up control of an industrial-scale evaporation system with hybrid dynamics

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CONTROL ENGINEERING PRACTICE 2008年第8期16卷 976-990页

作者： Sonntag, Christian Su, Wanjing Stursberg, Olaf Engell, Sebastian Univ Dortmund BCI AST Proc Control Lab D-44221 Dortmund Germany Tech Univ Munich EI LSR Inst Automat Control Engn D-80290 Munich Germany

In this paper, the task to start the operation of an evaporation system with hybrid dynamics is considered. The evaporator system was provided as a benchmark for hybrid control by a major chemical company. Rigorous modeling gives rise to a hybrid automaton with high-dimensional nonlinear DAE dynamics that describe the continuous evolution in different discrete modes of operation. The problem of optimized start-up is solved by a branch-and-bound algorithm with embedded nonlinear dynamic optimization over a finite look-ahead horizon. The nonlinear optimization problems are solved by nonlinear programming and by evolutionary algorithms. Important elements of this formulation of the optimization problems are the introduction of a dynamic choice of the time intervals over which the zero-order hold controls are constant and the utilization of tailored penalty functions in order to obtain solutions which are close to the bounds of the feasible state regions. The two approaches are compared with respect to their performance for the evaporation system. 2007 Elsevier Ltd. All rights reserved.

关键词： hybrid systems processing systems large-scale systems optimal control nonlinear programming evolutionary algorithms

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On the number of inner iterations per outer iteration of a globally convergent algorithm for optimization with general nonlinear inequality constraints and simple bounds

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COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 41-69页

作者： Conn, AR Gould, N Toint, PL RUTHERFORD APPLETON LAB CHILTONOXONENGLAND FAC UNIV NOTRE DAME PAIX DEPT MATHNAMURBELGIUM

This paper considers the number of inner iterations required per outer iteration for the algorithm proposed by Conn er al. [9]. We show that asymptotically, under suitable reasonable assumptions, a single inner iterat... 详细信息

关键词： nonlinear optimization inequality constraints barrier methods complexity

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Iterative computation of negative curvature directions in large scale optimization

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COMPUTATIONAL optimization AND APPLICATIONS 2007年第1期38卷 81-104页

作者： Fasano, Giovanni Roma, Massimo Univ Roma La Sapienza Dipartimento Informat & Sistemist A Ruberti Rome Italy Ist Nazl & Esperienze Architettura Navale INS Rome Italy

In this paper we deal with the iterative computation of negative curvature directions of an objective function, within large scale optimization frameworks. In particular, suitable directions of negative curvature of the objective function represent an essential tool, to guarantee convergence to second order critical points. However, an '' adequate '' negative curvature direction is often required to have a good resemblance to an eigenvector corresponding to the smallest eigenvalue of the Hessian matrix. Thus, its computation may be a very difficult task on large scale problems. Several strategies proposed in literature compute such a direction relying on matrix factorizations, so that they may be inefficient or even impracticable in a large scale setting. On the other hand, the iterative methods proposed either need to store a large matrix, or they need to rerun the recurrence. On this guideline, in this paper we propose the use of an iterative method, based on a planar Conjugate Gradient scheme. Under mild assumptions, we provide theory for using the latter method to compute adequate negative curvature directions, within optimization frameworks. In our proposal any matrix storage is avoided, along with any additional rerun.

关键词： Conjugate Gradient method large scale optimization negative curvature directions convergence to second order critical points

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