Books on the topic 'Quadratic programmin'

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

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

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

Schrage, Linus. Linear, integer, and quadratic programming with LINDO. 3rd ed. Palo Alto, CA: Scientific Press, 1986.

Education, Alberta Alberta, ed. Mathematics 30: Quadratic relations. 2nd ed. [Edmonton]: Distance Learning, Alberta Education, 1991.

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

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.

Dostál, Zdeněk. Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities. Boston, MA: Springer-Verlag US, 2009.

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.

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

Axehill, Daniel. Applications of integer quadratic programming in control and communication. Linko ping: Dept. of Electrical Engineering, Linko ping University, 2005.

Machielsen, K. C. P. Numerial solution of optimal control problems with state constraints by sequential quadratic programming in function space. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1988.

W, Longman Richard, and Langley Research Center, eds. Optimized system identification. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

W, Longman Richard, and Langley Research Center, eds. Optimized system identification. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

Institute for Computer Applications in Science and Engineering., ed. An interior point algorithm for the general nonlinear programming problem with trust region globalization. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1996.

Gurwitz, Chaya Bleich. Sequential quadratic programming methods based on approximating a projected Hessian matrix. New York: Courant Institute of Mathematical Sciences, New York University, 1986.

Zaghloul, Fathia. A comparative analysis of some methods for solving quadratic programming problems. Cairo: Salah Salem St-Nasr Ctty, 1988.

Hertog, D. den. Interior point approach to linear, quadratic, and convex programming: Algorithms and complexity. Dordrecht: Kluwer Academic Publishers, 1994.

Machielsen, K. C. P. Numerical solution of optimal control problems with state constraints by sequential quadratic programming function space. Amsterdam: Centrum voor Wiskunde en Informatica, 1988.

Schrage, Linus. User's manual for linear, integer and quadratic programming with LINDO release 5.0. San Francisco: Scientific Press, 1991.

Hertog, D. Interior Point Approach to Linear, Quadratic and Convex Programming: Algorithms and Complexity. Dordrecht: Springer Netherlands, 1994.

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

N, Patnaik Surya, and United States. National Aeronautics and Space Administration., eds. Comparative evaluation of different optimization algorithms for structural design applications. [Washington, DC: National Aeronautics and Space Administration, 1996.

A, Gabriele Gary, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. An investigation of new methods for estimating parameter sensitivities. [Washington, D.C.?]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.

A, Gabriele Gary, and United States. National Aeronautics and Space Administration., eds. An investigation of new methods for estimating parameter sensitivities. Troy, N.Y: Dept. of Mechanical Engineering, Aeronautical Engineering & Mechanics, Rensselaer Polytechnic Institute, 1988.

Lefkoff, Lawrence J. AQMAN: Linear and quadratic programming matrix generator using two-dimensional ground-water flow simulation for aquifer management modeling. Menlo Park, Calif: Dept. of the Interior, U.S. Geological Survey, 1987.

Machielsen, Kees Caspert Peter. Numerical Solution of Optimal Control Problems with State Constraints by Sequential Quadratic Programming in Function Space: Proefschrift. Helmond: Dissertatiedrukkerij Wibro, 1987.

L, Simon Donald, and NASA Glenn Research Center, eds. Kalman filtering with inequality constraints for turbofan engine health estimation. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2003.

J, Swetits John, and United States. National Aeronautics and Space Administration., eds. Automation of reverse engineering process in aircraft modeling and related optimization problems: Progress report for the period ended December 1994. Norfolk, VA: Old Dominion Research Foundation, 1994.

Norman, D. DBase III plus programmes for estimating plant populations and yields from plot quadrat data. Gabarone: Agricultural Technology Improvement Project, 1990.

Arian, Eyal. Approximation of the Newton step by a defect correction process. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

A, Batterman, Sachs E. W, and Institute for Computer Applications in Science and Engineering., eds. Approximation of the Newton step by a defect correction process. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

A, Batterman, Sachs E. W, and Institute for Computer Applications in Science and Engineering., eds. Approximation of the Newton step by a defect correction process. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

service), SpringerLink (Online, ed. Linear-Quadratic Controls in Risk-Averse Decision Making: Performance-Measure Statistics and Control Decision Optimization. New York, NY: Springer New York, 2013.

1957-, Bonnans J. F., ed. Numerical optimization: Theoretical and practical aspects. Berlin: Springer, 2003.

Optimal Quadratic Programming Algorithms. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b138610.

Best, Michael J. Quadratic Programming with Computer Programs. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315120881.

Best, Michael J. Quadratic Programming with Computer Programs. Taylor & Francis Group, 2017.

Best, Michael J. Quadratic Programming with Computer Programs. Taylor & Francis Group, 2017.

Quadratic Programming with Computer Programs. Taylor & Francis Group, 2023.

Best, Michael J. Quadratic Programming with Computer Programs. Taylor & Francis Group, 2017.

Best, Michael J. Quadratic Programming with Computer Programs. Taylor & Francis Group, 2017.

Best, Michael J. Quadratic Programming with Computer Programs. Taylor & Francis Group, 2017.

Quadratic Programming and Affine Variational Inequalities. New York: Springer-Verlag, 2005. http://dx.doi.org/10.1007/b105061.

Lindo: Linear, Integer, and Quadratic Programming. Course Technology, 1998.

Schrage, Linus. LINDO (Linear, Integer and Quadratic Programming). 4th ed. The Scientific Press, 1989.

Al-Saket, Amal Hikmat. An algorithm with degeneracy resolution for solving certain quadratic programming problems. 1985.

Schrage, Linus. User's Manual for Linear, Integer, & Quadratic Programming with Lindo. 3rd ed. Course Technology, 1986.

Quadratic Programming and Affine Variational Inequalities: A Qualitative Study. Springer London, Limited, 2005.

Optimal Quadratic Programming Algorithms Springer Optimization and Its Applications. Springer, 2010.