Nonlinear Programming, 4 focuses on linear, quadratic, and nonlinear programming, unconstrained minimization, nonsmooth and discrete optimization, ellipsoidal methods, linear complementarity problems, and software evaluation. The selection first elaborates on an upper triangular matrix method for quadratic programming, solving quadratic programs by an exact penalty function, and QP-based methods for large-scale nonlinearly constrained optimization. Discussions focus on large-scale linearly constrained optimization, search direction for superbasic variables, finite convergence, basic properties, comparison of three active set methods, and QP-based methods for dense problems. The book then examines an iterative linear programming algorithm based on an augmented Lagrangian and iterative algorithms for singular minimization problems. The publication ponders on the derivation of symmetric positive definite secant updates, preconditioned conjugate gradient methods, and finding the global minimum of a function of one variable using the method of constant signed higher order derivatives. Topics include effects of calculation errors, application to polynomial minimization, using moderate additional storage, updating Cholesky factors, and utilizing sparse second order information. The selection is a valuable source of data for researchers interested in nonlinear programming.

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Yes, you can access Nonlinear Programming 4 by Olvi L. Mangasarian,Robert R. Meyer,Stephen M. Robinson in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Academic Press

Year

2014

ISBN

9781483260174

Topic

Mathematics

Subtopic

Probability & Statistics

Index

Mathematics

Front Cover
Nonlinear Programming 4
Copyright Page
Table of Contents
CONTRIBUTORS
PREFACE
CHAPTER 1. AN UPPER TRIANGULAR MATRIX METHOD FOR QUADRATIC PROGRAMMING
CHAPTER 2. SOLVING QUADRATIC PROGRAMS BY AN EXACT PENALTY FUNCTION
CHAPTER 3. QP–BASED METHODS FOR LARGE-SCALE NONLINEARLY CONSTRAINED OPTIMIZATION
CHAPTER 4. NUMERICAL EXPERIMENTS WITH AN EXACT L1 PENALTY FUNCTION METHOD
CHAPTER 5. AN ITERATIVE LINEAR PROGRAMMING ALGORITHM BASED ON AN AUGMENTED LAGRANGIAN
CHAPTER 6. ITERATIVE ALGORITHMS FOR SINGULAR MINIMIZATION PROBLEMS
CHAPTER 7. A NEW DERIVATION OF SYMMETRIC POSITIVE DEFINITE SECANT UPDATES
CHAPTER 8. ON PRECONDITIONED CONJUGATE GRADIENT METHODS
CHAPTER 9. FINDING THE GLOBAL MINIMUM OF A FUNCTION OF ONE VARIABLE USING THE METHOD OF CONSTANT SIGNED HIGHER ORDER DERIVATIVES
CHAPTER 10. ON A BUNDLE ALGORITHM FOR NONSMOOTH OPTIMIZATION
CHAPTER 11. CONVERGENCE RESULTS IN A CLASS OFVARIABLE METRIC SUBGRADIENT METHODS1
CHAPTER 12. MONOTROPIC PROGRAMMING: DESCENT ALGORITHMS AND DUALITY
CHAPTER 13. APPROXIMATION AND CONVERGENCE IN NONLINEAR OPTIMIZATION
CHAPTER 14. UPPER PLANES OF QUADRATIC 0-1 FUNCTIONS AND STABILITY IN GRAPHS
CHAPTER 15. ON THE EXISTENCE OF FAST APPROXIMATION SCHEMES
CHAPTER 16. POLYNOMIALLY BOUNDED ELLIPSOID ALGORITHMS FOR CONVEX QUADRATIC PROGRAMMING
CHAPTER 17. THE IMPLICIT COMPLEMENTARITY PROBLEM
CHAPTER 18. METHODS FOR EVALUATING NONLINEAR PROGRAMMING SOFTWARE
SUBJECT INDEX