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Littérature scientifique sur le sujet « Quadratic programmin »

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Publié le 1 avril 2023

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Articles de revues sur le sujet "Quadratic programmin"

Majeed, Amir Sabir, et Fadhil Salman Abed. « A Proposed Method to Solve Quadratic Fractional Programming Problem by Converting to Double Linear Programming ». Journal of Zankoy Sulaimani - Part A 19, n^o 1 (5 juin 2016) : 239–49. http://dx.doi.org/10.17656/jzs.10602.

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Yamashita, Hiroshi, et Hiroshige Dan. « GLOBAL CONVERGENCE OF A TRUST REGION SEQUENTIAL QUADRATIC PROGRAMMING METHOD ». Journal of the Operations Research Society of Japan 48, n^o 1 (2005) : 41–56. http://dx.doi.org/10.15807/jorsj.48.41.

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Turlach, Berwin A., et Stephen J. Wright. « Quadratic programming ». Wiley Interdisciplinary Reviews : Computational Statistics 7, n^o 2 (19 janvier 2015) : 153–59. http://dx.doi.org/10.1002/wics.1344.

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Serna-Diaz, Raquel, Raimundo Santos Leite et Paulo J. S. Silva. « A mixed quadratic programming model for a robust support vector machine ». Selecciones Matemáticas 8, n^o 1 (30 juin 2021) : 27–36. http://dx.doi.org/10.17268/sel.mat.2021.01.03.

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Sulaiman, Nejmaddin A., et Maher A. Nawkhass. « Using Standard Division to Solve Multi- Objective Quadratic Fractional Programming Problems ». Journal of Zankoy Sulaimani - Part A 18, n^o 3 (5 juin 2016) : 157–64. http://dx.doi.org/10.17656/jzs.10544.

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Sulaiman, Najmaddin A., et Basiya K. Abulrahim. « Arithmetic Average Transformation Technique to Solve Multi-Objective Quadratic Programming Problem ». Journal of Zankoy Sulaimani - Part A 15, n^o 1 (17 décembre 2012) : 57–69. http://dx.doi.org/10.17656/jzs.10233.

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Wilson, G. « Quadratic programming analogs ». IEEE Transactions on Circuits and Systems 33, n^o 9 (septembre 1986) : 907–11. http://dx.doi.org/10.1109/tcs.1986.1086021.

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Boggs, Paul T., et Jon W. Tolle. « Sequential Quadratic Programming ». Acta Numerica 4 (janvier 1995) : 1–51. http://dx.doi.org/10.1017/s0962492900002518.

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Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results.

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Beck, Amir. « Quadratic Matrix Programming ». SIAM Journal on Optimization 17, n^o 4 (janvier 2007) : 1224–38. http://dx.doi.org/10.1137/05064816x.

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Koyuncu, Nursel. « Computation of Parameters Using Genetic Algorithm and Sequential Quadratic Programming in Sampling ». International Journal of Computer Theory and Engineering 7, n^o 5 (octobre 2015) : 394–97. http://dx.doi.org/10.7763/ijcte.2015.v7.992.

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Thèses sur le sujet "Quadratic programmin"

Lau, Karen Karman School of Mathematics UNSW. « Multistage quadratic stochastic programming ». Awarded by:University of New South Wales. School of Mathematics, 1999. http://handle.unsw.edu.au/1959.4/32672.

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Multistage stochastic programming is an important tool in medium to long term planning where there are uncertainties in the data. In this thesis, we consider a special case of multistage stochastic programming in which each subprogram is a convex quadratic program. The results are also applicable if the quadratic objectives are replaced by convex piecewise quadratic functions. Convex piecewise quadratic functions have important application in financial planning problems as they can be used as very flexible risk measures. The stochastic programming problems can be used as multi-period portfolio planning problems tailored to the need of individual investors. Using techniques from convex analysis and sensitivity analysis, we show that each subproblem of a multistage quadratic stochastic program is a polyhedral piecewise quadratic program with convex Lipschitz objective. The objective of any subproblem is differentiable with Lipschitz gradient if all its descendent problems have unique dual variables, which can be guaranteed if the linear independence constraint qualification is satisfied. Expression for arbitrary elements of the subdifferential and generalized Hessian at a point can be calculated for quadratic pieces that are active at the point. Generalized Newton methods with linesearch are proposed for solving multistage quadratic stochastic programs. The algorithms converge globally. If the piecewise quadratic objective is differentiable and strictly convex at the solution, then convergence is also finite. A generalized Newton algorithm is implemented in Matlab. Numerical experiments have been carried out to demonstrate its effectiveness. The algorithm is tested on random data with 3, 4 and 5 stages with a maximum of 315 scenarios. The algorithm has also been successfully applied to two sets of test data from a capacity expansion problem and a portfolio management problem. Various strategies have been implemented to improve the efficiency of the proposed algorithm. We experimented with trust region methods with different parameters, using an advanced solution from a smaller version of the original problem and sorting the stochastic right hand sides to encourage faster convergence. The numerical results show that the proposed generalized Newton method is a highly accurate and effective method for multistage quadratic stochastic programs. For problems with the same number of stages, solution times increase linearly with the number of scenarios.

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Ilyes, Amy Louise. « Using linear programming to solve convex quadratic programming problems ». Case Western Reserve University School of Graduate Studies / OhioLINK, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=case1056644216.

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Grainger, Daniel John. « Contributions to Quadratic 0 -1 Programming ». Thesis, Lancaster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518188.

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Wang, Xianzhi. « Resolution of Ties in Parametric Quadratic Programming ». Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1199.

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We consider the convex parametric quadratic programming problem when the end of the parametric interval is caused by a multiplicity of possibilities ("ties"). In such cases, there is no clear way for the proper active set to be determined for the parametric analysis to continue. In this thesis, we show that the proper active set may be determined in general by solving a certain non-parametric quadratic programming problem. We simplify the parametric quadratic programming problem with a parameter both in the linear part of the objective function and in the right-hand side of the constraints to a quadratic programming without a parameter. We break the analysis into three parts. We first study the parametric quadratic programming problem with a parameter only in the linear part of the objective function, and then a parameter only in the right-hand side of the constraints. Each of these special cases is transformed into a quadratic programming problem having no parameters. A similar approach is then applied to the parametric quadratic programming problem having a parameter both in the linear part of the objective function and in the right-hand side of the constraints.

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Byrne, Susan Jane. « Quadratic programming using complementarity and orthogonal factorization ». Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/8893.

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Morales-Perez, Jose Luis. « Computational methods for large-scale quadratic programming ». Thesis, Imperial College London, 1993. http://hdl.handle.net/10044/1/7511.

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For theoretical and practical reasons, quadratic programming problems have attracted the interest of the mathematical programming community. They naturally arise from applications and as subproblems in other numerical techniques. However most existing techniques, designed for solving small and dense problems, tend to be prohibitively expensive when applied directly to solve large-scale problems. In this work we explore methods suitable for solving large-scale sparse convex quadratic programming problems. An interior-point primal-dual algorithmic framework and its computational implementation are presented in the first part of this work. Primal and dual updates are computed at each step by iteratively solving the linear systems posed by the classical method of barriers using a preconditioned Krylov-subspace method. Several variants are suggested by a Taylor approximation of the central path. A truncated Newton strategy has been implemented in order to achieve a significant reduction in the CPU time. In the second part, sparse implementations for Lemke's algorithm and a row-action algorithm based on diagonal approximations of the Hessian, are suggested. Lemke's algorithm implementation is based on updating the sparse LU factorization of a matrix representing the basis at the current step. The implementation of the row-action algorithm relies on the efficient solution of single-constrained diagonal subproblems. In order to compare the relative merits of our implementations, numerical experimentation is conducted on two sets of problems that use randomly generated Hessian matrices and constraints taken from a subset of the netlib problems. Several aspects are studied: the use of iterative linear algebra for solving the linear systems of equations posed by the interior-point variants, the impact on the computational resources (memory and CPU) when different approaches are used to solve large scale problems, and finally, the effectiveness of a second order correction and the truncated Newton strategy implemented in the interior-point methods.

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Axehill, Daniel. « Integer quadratic programming for control and communcation / ». Linköping : Department of Electrical Engineering, Linköping University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10642.

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Jakee, Khan Md Kamall. « Computational convex analysis using parametric quadratic programming ». Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45182.

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The class of piecewise linear-quadratic (PLQ) functions is a very important class of functions in convex analysis since the result of most convex operators applied to a PLQ function is a PLQ function. Although there exists a wide range of algorithms for univariate PLQ functions, recent work has focused on extending these algorithms to PLQ functions with more than one variable. First, we recall a proof in [Convexity, Convergence and Feedback in Optimal Control, Phd thesis, R. Goebel, 2000] that PLQ functions are closed under partial conjugate computation. Then we use recent results on parametric quadratic programming (pQP) to compute the inf-projection of any multivariate convex PLQ function. We implemented the algorithm for bivariate PLQ functions, and modi ed it to compute conjugates. We provide a complete space and time worst-case complexity analysis and show that for bivariate functions, the algorithm matches the performance of [Computing the Conjugate of Convex Piecewise Linear-Quadratic Bivariate Functions, Bryan Gardiner and Yves Lucet, Mathematical Programming Series B, 2011] while being easier to extend to higher dimensions.

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Axehill, Daniel. « Integer Quadratic Programming for Control and Communication ». Doctoral thesis, Linköpings universitet, Reglerteknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10642.

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The main topic of this thesis is integer quadratic programming with applications to problems arising in the areas of automatic control and communication. One of the most widespread modern control methods is Model Predictive Control (MPC). In each sampling time, MPC requires the solution of a Quadratic Programming (QP) problem. To be able to use MPC for large systems, and at high sampling rates, optimization routines tailored for MPC are used. In recent years, the range of application of MPC has been extended to so-called hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem, which is known to have a computational complexity which grows exponentially in the number of binary optimization variables. In modern communication systems, multiple users share a so-called multi-access channel. To estimate the information originally sent, a maximum likelihood problem involving binary variables can be solved. The process of simultaneously estimating the information sent by multiple users is called Multiuser Detection (MUD). In this thesis, the problem to efficiently solve MIQP problems originating from MPC and MUD is addressed. Four different algorithms are presented. First, a polynomial complexity preprocessing algorithm for binary quadratic programming problems is presented. By using the algorithm, some, or all, binary variables can be computed efficiently already in the preprocessing phase. In numerical experiments, the algorithm is applied to unconstrained MPC problems with a mixture of real valued and binary valued control signals, and the result shows that the performance gain can be significant compared to solving the problem using branch and bound. The preprocessing algorithm has also been applied to the MUD problem, where simulations have shown that the bit error rate can be significantly reduced compared to using common suboptimal algorithms. Second, an MIQP algorithm tailored for MPC is presented. The algorithm uses a branch and bound method where the relaxed node problems are solved by a dual active set QP algorithm. In this QP algorithm, the KKT systems are solved using Riccati recursions in order to decrease the computational complexity. Simulation results show that both the proposed QP solver and MIQP solver have lower computational complexity compared to corresponding generic solvers. Third, the dual active set QP algorithm is enhanced using ideas from gradient projection methods. The performance of this enhanced algorithm is shown to be comparable with the existing commercial state-of-the-art QP solver \cplex for some random linear MPC problems. Fourth, an algorithm for efficient computation of the search directions in an SDP solver for a proposed alternative SDP relaxation applicable to MPC problems with binary control signals is presented. The SDP relaxation considered has the potential to give a tighter lower bound on the optimal objective function value compared to the QP relaxation that is traditionally used in branch and bound for these problems, and its computational performance is better than the ordinary SDP relaxation for the problem. Furthermore, the tightness of the different relaxations is investigated both theoretically and in numerical experiments.
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Ahlbom, Daniel. « Quadratic Programming Models in Strategic Sourcing Optimization ». Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-334227.

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Strategic sourcing allows for optimizing purchases on a large scale.Depending on the requirements of the client and the offers provided forthem, finding an optimal or even a near-optimal solution can become computationally hard. Mixed integer programming (MIP), where theproblem is modeled as a set of linear expressions with an objectivefunction for which an optimal solution results in a minimum objectivevalue, is particularly suitable for finding competitive results. However, given the research and improvements continually being made for quadratic programming (QP), which allows for objective functions with quadratic expressions as well, comparing runtimes and objective values for finding optimal and approximate solutions is advised: for hard problems, applying the correct methods may decrease runtimes by severalorders of magnitude. In this report, comparisons between MIP and QPmodels used in four different problems with three different solverswere made, measuring both optimization and approximation performance interms of runtimes and objective values. Experiments showed that while QP holds an advantage over MIP in some cases, it is not consistentlyefficient enough to provide a significant improvement in comparison with, for example, using a different solver.

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Livres sur le sujet "Quadratic programmin"

Gould, N. I. M. Preprocessing for quadratic programming. Chilton : Rutherford Appleton Laboratory, 2002.

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Kraft, Dieter. A software package for sequential quadratic programming. Koln : DFVLR, 1988.

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Coleman, Thomas F. An interior Newton method for quadratic programming. Ithaca, N.Y : Cornell Theory Center, Cornell University, 1993.

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Schrage, Linus. Linear, integer, and quadratic programming with LINDO. 3^e éd. Palo Alto, CA : Scientific Press, 1986.

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Education, Alberta Alberta, dir. Mathematics 30 : Quadratic relations. 2^e éd. [Edmonton] : Distance Learning, Alberta Education, 1991.

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Schrage, Linus. Linear, integer and quadratic programming with LINDO : User's manual. 2^e éd. Palo Alto, Calif : Scientific Press, 1985.

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Dodu, J. C. Méthodes de quasi-Newton en optimisation non linéaire. Clamart : Electricité de France, Direction des études et recherches, Service études de réseaux, Département Méthodes d'optimisation et de simulation, 1990.

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service), SpringerLink (Online, dir. Optimal Quadratic Programming Algorithms : With Applications to Variational Inequalities. Boston, MA : Springer-Verlag US, 2009.

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den Hertog, D. Interior Point Approach to Linear, Quadratic and Convex Programming. Dordrecht : Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1134-8.

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Gould, N. I. M. Numerical methods for large-scale non-convex quadratic programming. Chilton : Rutherford Appleton Laboratory, 2001.

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Chapitres de livres sur le sujet "Quadratic programmin"

Bhatti, M. Asghar. « Quadratic Programming ». Dans Practical Optimization Methods, 495–579. New York, NY : Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-0501-2_8.

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Vanderbei, Robert J. « Quadratic Programming ». Dans International Series in Operations Research & ; Management Science, 407–23. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74388-2_24.

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Horst, Reiner, Panos M. Pardalos et Nguyen V. Thoai. « Quadratic Programming ». Dans Nonconvex Optimization and Its Applications, 49–107. Boston, MA : Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-0015-5_2.

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Murty, Katta G. « Quadratic programming ». Dans Encyclopedia of Operations Research and Management Science, 655–61. New York, NY : Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_838.

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Vanderbei, Robert J. « Quadratic Programming ». Dans International Series in Operations Research & ; Management Science, 415–31. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39415-8_24.

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Fletcher, R. « Quadratic Programming ». Dans Practical Methods of Optimization, 229–58. Chichester, West Sussex England : John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118723203.ch10.

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Andrei, Neculai. « Quadratic Programming ». Dans Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology, 243–68. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58356-3_10.

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Vanderbei, Robert J. « Quadratic Programming ». Dans International Series in Operations Research & ; Management Science, 395–411. Boston, MA : Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5662-3_23.

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Vanderbei, Robert J. « Quadratic Programming ». Dans International Series in Operations Research & ; Management Science, 363–78. Boston, MA : Springer US, 2014. http://dx.doi.org/10.1007/978-1-4614-7630-6_24.

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Kythe, Prem K. « Quadratic Programming ». Dans Elements of Concave Analysis and Applications, 217–32. Boca Raton, Florida : CRC Press, [2018] : Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315202259-9.

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Actes de conférences sur le sujet "Quadratic programmin"

Tami, Y., K. Sebaa, M. Lahdeb et H. Nouri. « Mixed-Integer Quadratic Constrained Programming versus Quadratic Programming Methods for Distribution Network Reconfiguration ». Dans 2019 International Conference on Advanced Electrical Engineering (ICAEE). IEEE, 2019. http://dx.doi.org/10.1109/icaee47123.2019.9015181.

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Hu, Xiukun, Craig C. Douglas, Robert Lumley et Mookwon Seo. « GPU Accelerated Sequential Quadratic Programming ». Dans 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES). IEEE, 2017. http://dx.doi.org/10.1109/dcabes.2017.8.

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Jingyu Yang et Zhongyu Jiang. « Rectangle fitting via quadratic programming ». Dans 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2015. http://dx.doi.org/10.1109/mmsp.2015.7340875.

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Wang, Sheng-hua, et Qun-feng Zhang. « Quadratic Fuzzy Programming with Recourse ». Dans Proceedings of 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258983.

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Teng, Chin-Pun, et Jorge Angeles. « A Sequential-Quadratic-Programming Algorithm Using Orthogonal Decomposition With Gerschgorin Stabilization ». Dans ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8552.

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Abstract This paper introduces a new approach to sequential quadratic programming. Upon application of the Gerschgorin Theorem for the stabilization of the Hessian matrix and of the orthogonal-decomposition algorithm in the quadratic programming solution, this novel approach offers a faster computation and dispenses with a feasible initial guess.

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Luo, Zhijun, Guohua Chen et Lirong Wang. « A Modified Sequence Quadratic Programming Method for Nonlinear Programming ». Dans 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.42.

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Debnath, R., et H. Takahashi. « SVM Training : Second-Order Cone Programming versus Quadratic Programming ». Dans The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246822.

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Kumar, Suchet, et Madhuchanda Rakshit. « Interactive fuzzy programming procedure for Trilevel Quadratic Fractional Programming ». Dans 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN). IEEE, 2017. http://dx.doi.org/10.1109/ic3tsn.2017.8284484.

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Xinjilefu, X., Siyuan Feng et Christopher G. Atkeson. « Dynamic state estimation using Quadratic Programming ». Dans 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6942679.

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Waschburger, Ronaldo, Henrique Mohallem Paiva et Roberto Kawakami Harrop Galvao. « Input delay estimation using quadratic programming ». Dans 2011 9th IEEE International Conference on Control and Automation (ICCA). IEEE, 2011. http://dx.doi.org/10.1109/icca.2011.6137932.

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Rapports d'organisations sur le sujet "Quadratic programmin"

Eldersveld, S. K. Large-scale sequential quadratic programming algorithms. Office of Scientific and Technical Information (OSTI), septembre 1992. http://dx.doi.org/10.2172/10102731.

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Gill, Philip E., Walter Murray, Michael A. Saunders et Margaret H. Wright. Inertia-Controlling Methods for Quadratic Programming. Fort Belvoir, VA : Defense Technical Information Center, novembre 1988. http://dx.doi.org/10.21236/ada204664.

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Prieto, Francisco J. Sequential Quadratic Programming Algorithms for Optimization. Fort Belvoir, VA : Defense Technical Information Center, août 1989. http://dx.doi.org/10.21236/ada212800.

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Eldersveld, S. K. Large-scale sequential quadratic programming algorithms. Office of Scientific and Technical Information (OSTI), septembre 1992. http://dx.doi.org/10.2172/6932047.

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Prieto, F. Sequential quadratic programming algorithms for optimization. Office of Scientific and Technical Information (OSTI), août 1989. http://dx.doi.org/10.2172/5325989.

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Ponceleon, Dulce B. Barrier Methods for Large-Scale Quadratic Programming. Fort Belvoir, VA : Defense Technical Information Center, juin 1991. http://dx.doi.org/10.21236/ada238554.

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Boggs, P. T., P. D. Domich, J. E. Rogers et C. Witzgall. An interior-point method for linear and quadratic programming problems. Gaithersburg, MD : National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4556.

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Charnes, A., et J. Semple. Practical Error Bounds for a Class of Quadratic Programming Problems. Fort Belvoir, VA : Defense Technical Information Center, mars 1991. http://dx.doi.org/10.21236/ada243249.

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

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

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