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Journal articles on the topic 'Large-scale optimization methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Papadrakakis, M., N. D. Lagaros, Y. Tsompanakis, and V. Plevris. "Large scale structural optimization: Computational methods and optimization algorithms." Archives of Computational Methods in Engineering 8, no. 3 (2001): 239–301. http://dx.doi.org/10.1007/bf02736645.

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2

Gould, Nick, Dominique Orban, and Philippe Toint. "Numerical methods for large-scale nonlinear optimization." Acta Numerica 14 (April 19, 2005): 299–361. http://dx.doi.org/10.1017/s0962492904000248.

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Recent developments in numerical methods for solving large differentiable nonlinear optimization problems are reviewed. State-of-the-art algorithms for solving unconstrained, bound-constrained, linearly constrained and non-linearly constrained problems are discussed. As well as important conceptual advances and theoretical aspects, emphasis is also placed on more practical issues, such as software availability.

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3

Bottou, Léon, Frank E. Curtis, and Jorge Nocedal. "Optimization Methods for Large-Scale Machine Learning." SIAM Review 60, no. 2 (2018): 223–311. http://dx.doi.org/10.1137/16m1080173.

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4

Rogers, Jr., Jack W., and Robert A. Donnelly. "Potential Transformation Methods for Large-Scale Global Optimization." SIAM Journal on Optimization 5, no. 4 (1995): 871–91. http://dx.doi.org/10.1137/0805042.

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5

Leblanc, Larry J. "Optimization methods for large-scale multiechelon stockage problems." Naval Research Logistics 34, no. 2 (1987): 239–49. http://dx.doi.org/10.1002/1520-6750(198704)34:2<239::aid-nav3220340209>3.0.co;2-t.

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6

Bagattini, Francesco, Fabio Schoen, and Luca Tigli. "Clustering methods for large scale geometrical global optimization." Optimization Methods and Software 34, no. 5 (2019): 1099–122. http://dx.doi.org/10.1080/10556788.2019.1582651.

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7

TSOMPANAKIS, YIANNIS, and MANOLIS PAPADRAKAKIS. "EFFICIENT COMPUTATIONAL METHODS FOR LARGE-SCALE STRUCTURAL OPTIMIZATION." International Journal of Computational Engineering Science 01, no. 02 (2000): 331–54. http://dx.doi.org/10.1142/s146587630000015x.

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8

Bertsekas, Dimitri P. "Incremental proximal methods for large scale convex optimization." Mathematical Programming 129, no. 2 (2011): 163–95. http://dx.doi.org/10.1007/s10107-011-0472-0.

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9

Robinson, Daniel P. "Primal-Dual Active-Set Methods for Large-Scale Optimization." Journal of Optimization Theory and Applications 166, no. 1 (2015): 137–71. http://dx.doi.org/10.1007/s10957-015-0708-x.

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10

Fasano, Giovanni, and Massimo Roma. "Preconditioning Newton–Krylov methods in nonconvex large scale optimization." Computational Optimization and Applications 56, no. 2 (2013): 253–90. http://dx.doi.org/10.1007/s10589-013-9563-6.

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11

PAOLELLA, MARC S. "FAST METHODS FOR LARGE-SCALE NON-ELLIPTICAL PORTFOLIO OPTIMIZATION." Annals of Financial Economics 09, no. 02 (2014): 1440001. http://dx.doi.org/10.1142/s2010495214400016.

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Simple, fast methods for modeling the portfolio distribution corresponding to a non-elliptical, leptokurtic, asymmetric, and conditionally heteroskedastic set of asset returns are entertained. Portfolio optimization via simulation is demonstrated, and its benefits are discussed. An augmented mixture of normals model is shown to be superior to both standard (no short selling) Markowitz and the equally weighted portfolio in terms of out of sample returns and Sharpe ratio performance.

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12

Rogers, Jack W. "Potential transformation methods for large-scale constrained global optimization." Numerical Algorithms 9, no. 1 (1995): 13–24. http://dx.doi.org/10.1007/bf02143924.

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13

Hassan, Basim A., and Haneen A. Alashoor. "A Modified Spectral Methods for Large-Scale UnconStrained." Al-Mustansiriyah Journal of Science 29, no. 1 (2018): 127. http://dx.doi.org/10.23851/mjs.v29i1.576.

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A modified spectral methods for solving unconstrained optimization problems based on the formulae are derived which are given in [4, 5]. The proposed methods satisfied the descent condition. Moreover, we prove that the new spectral methods are globally convergent. The Numerical results show that the proposed methods effective by comparing with the FR-method.

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14

Schulz, Volker, and Hans Georg Book. "Partially reduced sqp methods for large-scale nonlinear optimization problems." Nonlinear Analysis: Theory, Methods & Applications 30, no. 8 (1997): 4723–34. http://dx.doi.org/10.1016/s0362-546x(97)00198-3.

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15

Zheng, Xiuyun, Hongwei Liu, and Aiguo Lu. "Sufficient descent conjugate gradient methods for large-scale optimization problems." International Journal of Computer Mathematics 88, no. 16 (2011): 3436–47. http://dx.doi.org/10.1080/00207160.2011.592938.

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16

Nie, Jiawang, and Li Wang. "Regularization Methods for SDP Relaxations in Large-Scale Polynomial Optimization." SIAM Journal on Optimization 22, no. 2 (2012): 408–28. http://dx.doi.org/10.1137/110825844.

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17

Gundersen, Geir, and Trond Steihaug. "On large-scale unconstrained optimization problems and higher order methods." Optimization Methods and Software 25, no. 3 (2010): 337–58. http://dx.doi.org/10.1080/10556780903239071.

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18

Gill, Philip E., and Michael W. Leonard. "Limited-Memory Reduced-Hessian Methods for Large-Scale Unconstrained Optimization." SIAM Journal on Optimization 14, no. 2 (2003): 380–401. http://dx.doi.org/10.1137/s1052623497319973.

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19

Carlson, D. A., A. Haurie, J. P. Vial, and D. S. Zachary. "Large-scale convex optimization methods for air quality policy assessment." Automatica 40, no. 3 (2004): 385–95. http://dx.doi.org/10.1016/j.automatica.2003.09.019.

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20

Schoen, Fabio, and Luca Tigli. "Efficient large scale global optimization through clustering-based population methods." Computers & Operations Research 127 (March 2021): 105165. http://dx.doi.org/10.1016/j.cor.2020.105165.

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21

Lukšan, Ladislav, Ctirad Matonoha, and Jan Vlček. "Interior point methods for large-scale nonlinear programming." Optimization Methods and Software 20, no. 4-5 (2005): 569–82. http://dx.doi.org/10.1080/10556780500140508.

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22

Qu, B. Y., Q. Zhou, J. M. Xiao, J. J. Liang, and P. N. Suganthan. "Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4197914.

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Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise

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23

MA, Tie-lin, and Dong-li MA. "Multidisciplinary Design-Optimization Methods for Aircrafts using Large-Scale System Theory." Systems Engineering - Theory & Practice 29, no. 9 (2009): 186–92. http://dx.doi.org/10.1016/s1874-8651(10)60073-7.

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24

Wang, Shun, Eric de Sturler, and Glaucio H. Paulino. "Large-scale topology optimization using preconditioned Krylov subspace methods with recycling." International Journal for Numerical Methods in Engineering 69, no. 12 (2007): 2441–68. http://dx.doi.org/10.1002/nme.1798.

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25

Annighofer, Bjorn, and Ernst Kleemann. "Large-Scale Model-Based Avionics Architecture Optimization Methods and Case Study." IEEE Transactions on Aerospace and Electronic Systems 55, no. 6 (2019): 3424–41. http://dx.doi.org/10.1109/taes.2019.2907394.

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26

Vlček, Jan, and Ladislav Lukšan. "Shifted limited-memory variable metric methods for large-scale unconstrained optimization." Journal of Computational and Applied Mathematics 186, no. 2 (2006): 365–90. http://dx.doi.org/10.1016/j.cam.2005.02.010.

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27

Abeynanda, Hansi K., and G. H. J. Lanel. "A Study on Distributed Optimization over Large-Scale Networked Systems." Journal of Mathematics 2021 (April 29, 2021): 1–19. http://dx.doi.org/10.1155/2021/5540262.

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Distributed optimization is a very important concept with applications in control theory and many related fields, as it is high fault-tolerant and extremely scalable compared with centralized optimization. Centralized solution methods are not suitable for many application domains that consist of large number of networked systems. In general, these large-scale networked systems cooperatively find an optimal solution to a common global objective during the optimization process. Thus, it gives us an opportunity to analyze distributed optimization techniques that is demanded in most distributed op

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28

Risbeck, Michael J., Christos T. Maravelias, James B. Rawlings, and Robert D. Turney. "Mixed-integer optimization methods for online scheduling in large-scale HVAC systems." Optimization Letters 14, no. 4 (2019): 889–924. http://dx.doi.org/10.1007/s11590-018-01383-9.

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29

Onwunalu, J. E. E., and L. J. J. Durlofsky. "A New Well-Pattern-Optimization Procedure for Large-Scale Field Development." SPE Journal 16, no. 03 (2011): 594–607. http://dx.doi.org/10.2118/124364-pa.

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Summary The optimization of large-scale multiwell field-development projects is challenging because the number of optimization variables and the size of the search space can become excessive. This difficulty can be circumvented by considering well patterns and then optimizing parameters associated with the pattern type and geometry. In this paper, we introduce a general framework for accomplishing this type of optimization. The overall procedure, which we refer to as well-pattern optimization (WPO), includes a new well-pattern description (WPD) incorporated into an underlying optimization meth

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30

Woo, Gillian Yi Han, Hong Seng Sim, Yong Kheng Goh, and Wah June Leong. "Spectral proximal method for solving large scale sparse optimization." ITM Web of Conferences 36 (2021): 04007. http://dx.doi.org/10.1051/itmconf/20213604007.

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In this paper, we propose to use spectral proximal method to solve sparse optimization problems. Sparse optimization refers to an optimization problem involving the ι0 -norm in objective or constraints. The previous research showed that the spectral gradient method is outperformed the other standard unconstrained optimization methods. This is due to spectral gradient method replaced the full rank matrix by a diagonal matrix and the memory decreased from Ο(n2) to Ο(n). Since ι0-norm term is nonconvex and non-smooth, it cannot be solved by standard optimization algorithm. We will solve the ι0 -n

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31

Jabbar, Hawraz N., and Basim A. Hassan. "Two-versions of descent conjugate gradient methods for large-scale unconstrained optimization." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1643. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1643-1649.

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<p>The conjugate gradient methods are noted to be exceedingly valuable for solving large-scale unconstrained optimization problems since it needn't the storage of matrices. Mostly the parameter conjugate is the focus for conjugate gradient methods. The current paper proposes new methods of parameter of conjugate gradient type to solve problems of large-scale unconstrained optimization. A Hessian approximation in a diagonal matrix form on the basis of second and third-order Taylor series expansion was employed in this study. The sufficient descent property for the proposed algorithm are p

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32

Chen, Yuzhong, Yang Yu, and Guolong Chen. "Shortest distance estimation in large scale graphs." Engineering Computations 31, no. 8 (2014): 1635–47. http://dx.doi.org/10.1108/ec-11-2012-0286.

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Purpose – Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies. Design/methodology/approach – Diff

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33

Jasa, John P., Benjamin J. Brelje, Justin S. Gray, Charles A. Mader, and Joaquim R. R. A. Martins. "Large-Scale Path-Dependent Optimization of Supersonic Aircraft." Aerospace 7, no. 10 (2020): 152. http://dx.doi.org/10.3390/aerospace7100152.

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Aircraft are multidisciplinary systems that are challenging to design due to interactions between the subsystems. The relevant disciplines, such as aerodynamic, thermal, and propulsion systems, must be considered simultaneously using a path-dependent formulation to assess aircraft performance accurately. In this paper, we construct a coupled aero-thermal-propulsive-mission multidisciplinary model to optimize supersonic aircraft considering their path-dependent performance. This large-scale optimization problem captures non-intuitive design trades that single disciplinary models and path-indepe

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34

Jusoh, Ibrahim, Mustafa Mamat, and Mohd Rivaie. "A new edition of conjugate gradient methods for large-scale unconstrained optimization." International Journal of Mathematical Analysis 8 (2014): 2277–91. http://dx.doi.org/10.12988/ijma.2014.44115.

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35

Lucidi, Stefano, Francesco Rochetich, and Massimo Roma. "Curvilinear Stabilization Techniques for Truncated Newton Methods in Large Scale Unconstrained Optimization." SIAM Journal on Optimization 8, no. 4 (1998): 916–39. http://dx.doi.org/10.1137/s1052623495295250.

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36

Lamberti, L., and C. Pappalettere. "Design optimization of large-scale structures with sequential linear programming." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 216, no. 8 (2002): 799–811. http://dx.doi.org/10.1243/09544060260171438.

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Design optimization of complex structures entails tasks that oppose the usual constraints on time and computational resources. However, using optimization techniques is very useful because it allows engineers to obtain a large set of designs at low computational cost. Among the different optimization methods, sequential linear programming (SLP) is very popular because of its simplicity and because linear solvers (e.g. Simplex) are easily available. In spite of the inherent theoretical simplicity, well-coded SLP algorithms may outperform more sophisticated optimization methods. This paper descr

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37

Leibfritz, F., and E. W. Sachs. "Inexact SQP Interior Point Methods and Large Scale Optimal Control Problems." SIAM Journal on Control and Optimization 38, no. 1 (1999): 272–93. http://dx.doi.org/10.1137/s0363012996298795.

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38

Matusov, Leonid. "Malticriteria design of a large-scale mechanical systems." E3S Web of Conferences 126 (2019): 00016. http://dx.doi.org/10.1051/e3sconf/201912600016.

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The design and optimization of a large-scale systems are the most difficalt problems. A large-scale system consists of a number of subsystems. For example, in a harvest for harvesting one can separate the following subsystems: the frame, driver's cab, platform, engine, transmission, and steering system. Different departments of the design office engaged in creating a machine optimize their ‘own’ subsystems, while ignoring others. A machine assembled from ‘autonomously optimal’ subsystems turns out to be far from perfect. A machine is a single whole. When improving one of its subsystems, we can

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39

Métivier, Ludovic, and Romain Brossier. "The SEISCOPE optimization toolbox: A large-scale nonlinear optimization library based on reverse communication." GEOPHYSICS 81, no. 2 (2016): F1—F15. http://dx.doi.org/10.1190/geo2015-0031.1.

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The SEISCOPE optimization toolbox is a set of FORTRAN 90 routines, which implement first-order methods (steepest-descent and nonlinear conjugate gradient) and second-order methods ([Formula: see text]-BFGS and truncated Newton), for the solution of large-scale nonlinear optimization problems. An efficient line-search strategy ensures the robustness of these implementations. The routines are proposed as black boxes easy to interface with any computational code, where such large-scale minimization problems have to be solved. Traveltime tomography, least-squares migration, or full-waveform invers

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40

Zhang, Jianguo, Yunhai Xiao, and Zengxin Wei. "Nonlinear Conjugate Gradient Methods with Sufficient Descent Condition for Large-Scale Unconstrained Optimization." Mathematical Problems in Engineering 2009 (2009): 1–16. http://dx.doi.org/10.1155/2009/243290.

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Two nonlinear conjugate gradient-type methods for solving unconstrained optimization problems are proposed. An attractive property of the methods, is that, without any line search, the generated directions always descend. Under some mild conditions, global convergence results for both methods are established. Preliminary numerical results show that these proposed methods are promising, and competitive with the well-known PRP method.

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41

Clark, S. J., C. Barnhart, and S. E. Kolitz. "Large-scale optimization planning methods for the distribution of United States army munitions." Mathematical and Computer Modelling 39, no. 6-8 (2004): 697–714. http://dx.doi.org/10.1016/s0895-7177(04)90549-3.

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42

ARBEL, AMI. "Large-scale optimization methods applied to the cutting stock problem of irregular shapes." International Journal of Production Research 31, no. 2 (1993): 483–500. http://dx.doi.org/10.1080/00207549308956738.

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43

Jakovetic, Dusan, Dragana Bajovic, Joao Xavier, and Jose M. F. Moura. "Primal–Dual Methods for Large-Scale and Distributed Convex Optimization and Data Analytics." Proceedings of the IEEE 108, no. 11 (2020): 1923–38. http://dx.doi.org/10.1109/jproc.2020.3007395.

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44

Baena, Daniel, Jordi Castro, and Antonio Frangioni. "Stabilized Benders Methods for Large-Scale Combinatorial Optimization, with Application to Data Privacy." Management Science 66, no. 7 (2020): 3051–68. http://dx.doi.org/10.1287/mnsc.2019.3341.

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The cell-suppression problem (CSP) is a very large mixed-integer linear problem arising in statistical disclosure control. However, CSP has the typical structure that allows application of the Benders decomposition, which is known to suffer from oscillation and slow convergence, compounded with the fact that the master problem is combinatorial. To overcome this drawback, we present a stabilized Benders decomposition whose master is restricted to a neighborhood of successful candidates by local-branching constraints, which are dynamically adjusted, and even dropped, during the iterations. Our e

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45

Roma, Massimo. "Dynamic scaling based preconditioning for truncated Newton methods in large scale unconstrained optimization." Optimization Methods and Software 20, no. 6 (2005): 693–713. http://dx.doi.org/10.1080/10556780410001727709.

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46

Yu, Gaohang, Lutai Guan, and Wufan Chen. "Spectral conjugate gradient methods with sufficient descent property for large-scale unconstrained optimization." Optimization Methods and Software 23, no. 2 (2008): 275–93. http://dx.doi.org/10.1080/10556780701661344.

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47

Liu, Zexian, and Hongwei Liu. "Several efficient gradient methods with approximate optimal stepsizes for large scale unconstrained optimization." Journal of Computational and Applied Mathematics 328 (January 2018): 400–413. http://dx.doi.org/10.1016/j.cam.2017.07.035.

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48

Björk, Kaj-Mikael, and Roger Nordman. "Solving large-scale retrofit heat exchanger network synthesis problems with mathematical optimization methods." Chemical Engineering and Processing: Process Intensification 44, no. 8 (2005): 869–76. http://dx.doi.org/10.1016/j.cep.2004.09.005.

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49

Bonettini, Silvia, Emanuele Galligani, and Valeria Ruggiero. "Inner solvers for interior point methods for large scale nonlinear programming." Computational Optimization and Applications 37, no. 1 (2007): 1–34. http://dx.doi.org/10.1007/s10589-007-9012-5.

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50

Arnold, E., P. Tatjewski, and P. Wołochowicz. "Two methods for large-scale nonlinear optimization and their comparison on a case study of hydropower optimization." Journal of Optimization Theory and Applications 81, no. 2 (1994): 221–48. http://dx.doi.org/10.1007/bf02191662.

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