Relevant bibliographies by topics / Sequential linear programming algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sequential linear programming algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Sequential linear programming algorithm"

1

Mulkay, E. L., and S. S. Rao. "Fuzzy Heuristics for Sequential Linear Programming." Journal of Mechanical Design 120, no. 1 (1998): 17–23. http://dx.doi.org/10.1115/1.2826669.

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Numerical implementations of optimization algorithms often use parameters whose values are not strictly determined by the derivation of the algorithm, but must fall in some appropriate range of values. This work describes how fuzzy logic can be used to “control” such parameters to improve algorithm performance. This concept is shown with the use of sequential linear programming (SLP) due to its simplicity in implementation. The algorithm presented in this paper implements heuristics to improve the behavior of SLP based on current iterate values of design constraints and changes in search direc
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2

Osborne, M. R., and R. S. Womersley. "Strong uniqueness in sequential linear programming." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 31, no. 4 (1990): 379–84. http://dx.doi.org/10.1017/s0334270000006731.

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AbstractIt is known that strong uniqueness can be used to prove second order convergence of the generalised Gauss-Newton algorithm. Formally this algorithm includes sequential linear programming as a special case. Here we show that the second order convergence result extends when the sequential linear programming algorithm is formulated appropriately. Also this discussion provides an example which shows that the assumption of Lipschitz continuity is necessary for the second order convergence result based on strong uniqueness.
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Fletcher, Roger. "A Sequential Linear Constraint Programming Algorithm for NLP." SIAM Journal on Optimization 22, no. 3 (2012): 772–94. http://dx.doi.org/10.1137/110844362.

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Gangammanavar, Harsha, and Suvrajeet Sen. "Stochastic Dynamic Linear Programming: A Sequential Sampling Algorithm for Multistage Stochastic Linear Programming." SIAM Journal on Optimization 31, no. 3 (2021): 2111–40. http://dx.doi.org/10.1137/19m1290735.

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Lamberti, L., and C. Pappalettere. "An efficient Sequential Linear Programming algorithm for engineering optimization." Journal of Engineering Design 16, no. 3 (2005): 353–71. http://dx.doi.org/10.1080/09544820500115717.

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Lomi, Abraham, Awan Uji Krismanto, I. Made Wartana, and Dipu Sarkar. "A Numerically Robust Sequential Linear Programming Algorithm for Reactive Power Optimization." E3S Web of Conferences 188 (2020): 00002. http://dx.doi.org/10.1051/e3sconf/202018800002.

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A robust sequential primal-dual linear programming formulation for reactive power optimization is developed and discussed in this paper. The algorithm has the characteristic that no approximations or complicate control logic are required in the basic Sequential Linear Programming (SLP) formulation as used by other SLP algorithms reported in the literature. Transmission loss minimization is used as the primary objective. A secondary feasibility improvement objective is used which results in better feasible solution in comparison with the loss minimization objective especially when the initial b
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Marcotte, Patrice, and Jean-Pierre Dussault. "A Sequential Linear Programming Algorithm for Solving Monotone Variational Inequalities." SIAM Journal on Control and Optimization 27, no. 6 (1989): 1260–78. http://dx.doi.org/10.1137/0327064.

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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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Hu, Qing-Jie, and Ju-Zhou Hu. "A sequential quadratic programming algorithm for nonlinear minimax problems." Bulletin of the Australian Mathematical Society 76, no. 3 (2007): 353–68. http://dx.doi.org/10.1017/s0004972700039745.

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In this paper, an active set sequential quadratic programming algorithm with non-monotone line search for nonlinear minmax problems is presented. At each iteration of the proposed algorithm, a main search direction is obtained by solving a reduced quadratic program which always has a solution. In order to avoid the Maratos effect, a correction direction is yielded by solving the reduced system of linear equations. Under mild conditions without the strict complementarity, the global and superlinear convergence can be achieved. Finally, some preliminary numerical experiments are reported.
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Etoa Etoa, Jean Bosco. "A globally convergent sequential linear programming algorithm for mathematical programs with linear complementarity constraints." Journal of Information and Optimization Sciences 31, no. 5 (2010): 1011–39. http://dx.doi.org/10.1080/02522667.2010.10700008.

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Dissertations / Theses on the topic "Sequential linear programming algorithm"

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Tao, Ye. "Optimal power flow via quadratic modeling." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45766.

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Optimal power flow (OPF) is the choice tool for determining the optimal operating status of the power system by managing controllable devices. The importance of the OPF approach has increased due to increasing energy prices and availability of more control devices. Existing OPF approaches exhibit shortcomings. Current OPF algorithms can be classified into (a) nonlinear programming, (b) intelligent search methods, and (c) sequential algorithms. Nonlinear programming algorithms focus on the solution of the Kuhn-Tucker conditions; they require a starting feasible solution and the model includes a
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Mitradjieva-Daneva, Maria. "Feasible Direction Methods for Constrained Nonlinear Optimization : Suggestions for Improvements." Doctoral thesis, Linköping : Department of Mathematics, Linköping University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8811.

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ASSIS, Lilian Pureza de. "Otimização de estruturas reticuladas planas com comportamento geometricamente não linear." Universidade Federal de Goiás, 2006. http://repositorio.bc.ufg.br/tede/handle/tde/678.

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Made available in DSpace on 2014-07-29T15:03:39Z (GMT). No. of bitstreams: 1 lilian pureza.pdf: 2774999 bytes, checksum: 2a074d04ee02c7e1c87fdbe8c2c68ef6 (MD5) Previous issue date: 2006-10-20<br>The aim of this work is to present a formulation and corresponding computational implementation for sizing optimization of plane frames and cable-stayed columns considering geometric non liner behavior. The structural analysis is based on the finite element method using the updated lagrangian approach for plane frame and cable elements, which are represented by plane truss elements. The non linear sy
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Crowe, Mitch. "Nonlinearly constrained optimization via sequential regularized linear programming." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/29648.

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This thesis proposes a new active-set method for large-scale nonlinearly con strained optimization. The method solves a sequence of linear programs to generate search directions. The typical approach for globalization is based on damping the search directions with a trust-region constraint; our proposed ap proach is instead based on using a 2-norm regularization term in the objective. Numerical evidence is presented which demonstrates scaling inefficiencies in current sequential linear programming algorithms that use a trust-region constraint. Specifically, we show that the trust-region constr
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SARAVANAN, SHANKAR. "EVALUATION OF SPHERICITY USING MODIFIED SEQUENTIAL LINEAR PROGRAMMING." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1132343760.

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Lewis, Kemper E. "The adaptive linear programming algorithm : facilitating robust design." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/15949.

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Castillo, Ileana. "Some properties of the affine scaling algorithm." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/25459.

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Freund, Robert M. "Theoretical Efficiency of A Shifted Barrier Function Algorithm for Linear Programming." Massachusetts Institute of Technology, Operations Research Center, 1989. http://hdl.handle.net/1721.1/5185.

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This paper examines the theoretical efficiency of solving a standard-form linear program by solving a sequence of shifted-barrier problems of the form minimize cTx - n (xj + ehj) j.,1 x s.t. Ax = b , x + e h > , for a given and fixed shift vector h > 0, and for a sequence of values of > 0 that converges to zero. The resulting sequence of solutions to the shifted barrier problems will converge to a solution to the standard form linear program. The advantage of using the shiftedbarrier approach is that a starting feasible solution is unnecessary, and there is no need for a Phase I-Phase II appro
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Trigos, Federico. "A projective technique for accelerating convergence of the affine scaling algorithm for linear programming." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/23380.

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Wilbanks, John W. (John Winston). "Linear Unification." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc500971/.

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Efficient unification is considered within the context of logic programming. Unification is explained in terms of equivalence classes made up of terms, where there is a constraint that no equivalence class may contain more than one function term. It is demonstrated that several well-known "efficient" but nonlinear unification algorithms continually maintain the said constraint as a consequence of their choice of data structure for representing equivalence classes. The linearity of the Paterson-Wegman unification algorithm is shown largely to be a consequence of its use of unbounded lists of po
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Books on the topic "Sequential linear programming algorithm"

1

Bhavikatti, S. S. Structural optimisation using sequential linear programming. Vikas Publishing House Pvt., 2003.

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Currie, James D. The complexity of the simplex algorithm. Carleton University, Mathematics and Statistics, 1985.

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Blair, Charles. The iterative step in the linear programming algorithm of N. Karmarkar. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1985.

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Morton, David P. An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling. Naval Postgraduate School, 1994.

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Fujishige, Satoru. The minimum-norm-point algorithm applied to submodular function minimization and linear programming. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2006.

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Riefenberg, Jennifer S. A simplex-method-based algorithm for determining the source location of microseismic events. Dept. of the Interior, 1989.

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Riefenberg, Jennifer S. A simplex-method-based algorithm for determining the source location of microseismic events. U.S. Dept. of the Interior, Bureau of Mines, 1989.

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Rommelfanger, Heinrich. PC software FULPAL 2.0: An interactive algorithm for solving multicriteria fuzzy linear programs controlled by aspiration levels. Johann Wolfgang Goethe-Universität Frankfurt, Fachbereich Wirtschaftswissenschaften, 1995.

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Patnaik, Surya N. Structural optimization with approximate sensitivities. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1994.

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10

Coleman, Thomas F. A quadradically[sic]-convergent algorithm for the linear programming problem with lower and uppper [sic] bounds. Cornell Theory Center, Cornell University, 1990.

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Book chapters on the topic "Sequential linear programming algorithm"

1

Bentobache, Mohand, Mohamed Telli, and Abdelkader Mokhtari. "A Sequential Linear Programming Algorithm for Continuous and Mixed-Integer Nonconvex Quadratic Programming." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21803-4_3.

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Karloff, Howard. "Karmarkar’s Algorithm." In Linear Programming. Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4844-2_5.

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Karloff, Howard. "The Ellipsoid Algorithm." In Linear Programming. Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4844-2_4.

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Karloff, Howard. "The Simplex Algorithm." In Linear Programming. Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4844-2_2.

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Strayer, James K. "The Simplex Algorithm." In Linear Programming and Its Applications. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1009-2_3.

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Ploskas, Nikolaos, and Nikolaos Samaras. "Exterior Point Simplex Algorithm." In Linear Programming Using MATLAB®. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65919-0_10.

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Ploskas, Nikolaos, and Nikolaos Samaras. "Revised Primal Simplex Algorithm." In Linear Programming Using MATLAB®. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65919-0_8.

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Ploskas, Nikolaos, and Nikolaos Samaras. "Revised Dual Simplex Algorithm." In Linear Programming Using MATLAB®. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65919-0_9.

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Jiang, Chao, Xu Han, and Huichao Xie. "Interval Optimization Based on Sequential Linear Programming." In Nonlinear Interval Optimization for Uncertain Problems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8546-3_6.

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Chen, Danny Z., and Jinhui Xu. "Two-variable linear programming in parallel." In Algorithm Theory — SWAT'98. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054365.

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Conference papers on the topic "Sequential linear programming algorithm"

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Mulkay, Eric L., and Singiresu S. Rao. "Fuzzy Heuristics for Sequential Linear Programming." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/dac-3966.

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Abstract Numerical implementations of optimization algorithms often use parameters whose values are not strictly determined by the derivation of the algorithm, but must fall in some appropriate range of values. This work describes how fuzzy logic can be used to “control” such parameters to improve algorithms performance. This concept is shown with the use of sequential linear programming (SLP) due to its simplicity in implementation. The algorithm presented in this paper implements heuristics to improve the behavior of SLP based on current iterate values of design constraints and changes in se
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Xu, Zhiming, Yu Bai, and Shuning Wang. "Sequential Global Linear Programming Algorithm for Continuous Piecewise Linear Programming*." In 2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2018. http://dx.doi.org/10.1109/wcica.2018.8630336.

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Hsu, Yeh-Liang, Yu-Fa Lin, and Yu-Shuei Guo. "A Fuzzy Sequential Linear Programming Algorithm for Engineering Design Optimization." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0060.

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Abstract An optimization process can be viewed as a closed-loop control system. Traditional “controllers”, the numerical optimization algorithms, are usually “crisply” designed for well defined mathematical models. However, when applied to engineering design optimization problems in which function evaluations can be expensive and imprecise, very often the crisp algorithms will become impractical or will not converge. A common strategy for designers is to monitor the optimization process and keep “tuning” the process in an interactive manner, using their judgment on the information obtained fro
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Han, Jeongwoo, and Panos Papalambros. "A Sequential Linear Programming Coordination Algorithm for Analytical Target Cascading." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35361.

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Decomposition-based strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC, which coordinates the interactions among subproblems using sequential lineralizations. Linearity of subproblems is maintained using L∞ norms to measure deviations between targets and responses. A subproblem suspension strategy is used to temporarily suspend inclusion of subproble
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Tao, Ye, and A. P. Sakis Meliopoulos. "An sequential linear programming algorithm for security-constrained optimal power flow." In 2009 North American Power Symposium - NAPS. IEEE, 2009. http://dx.doi.org/10.1109/naps.2009.5484047.

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Chan, Kuei-Yuan, Steven J. Skerlos, and Panos Y. Papalambros. "An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84489.

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Optimal design problems with probabilistic constraints, often referred to as Reliability-Based Design Optimization (RBDO) problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using bo
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Lewis, Kemper, and Farrokh Mistree. "Foraging-Directed Adaptive Linear Programming: An Algorithm for Solving Nonlinear Mixed Discrete/Continuous Design Problems." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dac-1601.

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Abstract Design models often contain a combination of discrete, integer, and continuous variables. Previously, the Adaptive Linear Programming (ALP) Algorithm, which is based on sequential linearization, has been used to solve design models composed of continuous and Boolean variables. In this paper, we extend the ALP Algorithm using a discrete heuristic based on the analogy of an animal foraging for food. This algorithm for mixed discrete/continuous design problems integrates ALP and the foraging search and is called Foraging-directed Adaptive Linear Programming (FALP). Two design studies are
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Hsu, Yeh-Liang, Tzyh-Li Sun, and Li-Hwang Leu. "A Two-Stage Sequential Approximation Method for Non-Linear Discrete-Variable Optimization." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0026.

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Abstract A two-stage sequential approximation method is developed for non-linear discrete-variable optimization. The concept of this technique is similar to that of sequential linear programming (SLP), only in each iteration, the linear programming subproblem in the first stage is modified into a discrete programming subproblem in the second stage in order to solve for a discrete solution. SLP is often impractical when applied to engineering optimization problems with implicit constraints, because of the difficulties in choosing proper move limits. For this reason, in the second stage a “bound
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Tzannetakis, N., and P. Y. Papalambros. "An Active Set Sequential Linearization Algorithm for Nonlinear Design Optimization." In ASME 1987 Design Technology Conferences. American Society of Mechanical Engineers, 1987. http://dx.doi.org/10.1115/detc1987-0001.

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Abstract Solution of nonlinear design optimization problems via a sequence of linear programs is regaining attention for solving certain model classes, such as in structural design and chemical process design. An active set strategy modification of an algorithm by Palacios-Gomez is presented. A special interior linear programming algorithm with active set strategy is used also for solving the subproblem and generating the working set of the outer iterations. Examples are included.
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Xie, Shuiwei, and Warren F. Smith. "Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/cie-34400.

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In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Eng
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Reports on the topic "Sequential linear programming algorithm"

1

Todd, Michael J., and Yinyu Ye. A Centered Projective Algorithm for Linear Programming. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada192100.

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Tseng, Paul. A Very Simple Polynomial-Time Algorithm for Linear Programming. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada202502.

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Murray, W., and F. J. Prieto. A sequential quadratic programming algorithm using an incomplete solution of the subproblem. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10166655.

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Murray, Walter, and Francisco J. Prieto. A Sequential Quadratic Programming Algorithm Using an Incomplete Solution of the Subproblem. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada228829.

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Murray, Walter, and Francisco J. Prieto. A Sequential Quadratic Programming Algorithm Using An Incomplete Solution of the Subproblem. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada267216.

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Saltzman, Robert M. A Heuristic Ceiling Point Algorithm for General Integer Linear Programming. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada202285.

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Saltzman, Robert M., and Frederick S. Hillier. An Exact Ceiling Point Algorithm for General Integer Linear Programming. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada202286.

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Dennis, Jr, Morshedi J. E., Turner A. M., and Kathryn. A Variable-Metric Variant of the Karmarkar Algorithm for Linear Programming. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada453840.

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Zhang, Yin, and Richard A. Tapia. A Superlinearly Convergent Polynomial Primal-Dual Interior-Point Algorithm for Linear Programming. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada453104.

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Ye, Y., R. A. Tapia, and Y. Zhang. A Superlinearly Convergent O(square root of nL)-Iteration Algorithm for Linear Programming. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada452256.

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