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1.

Book

Book
Masanao Aoki
Published: New York : Macmillan, c1971  xvi, 335 p. ; 24 cm
Series: Macmillan series in applied computer sciences
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2.

Book

Book
Mordecai Avriel
Published: Englewood Cliffs, N.J. : Prentice-Hall, c1976  xv, 512 p. ; 24 cm
Series: Prentice-Hall series in automatic computation
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3.

Book

Book
Mokhtar S. Bazaraa, C.M. Shetty
Published: New York : Wiley, c1979  xiv, 560 p. ; 24 cm
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Table of Contents: Read more
Introduction. / Chapter 1:
Problem Statement and Basic Definitions / 1.1:
Illustrative Examples / 1.2:
Guidelines for Model Construction / 1.3:
Exercises
Notes and References
Convex Analysis. / Part 1:
Convex Sets. / Chapter 2:
Convex Hulls / 2.1:
Closure and Interior of a Set / 2.2:
Weierstrass's Theorem / 2.3:
Separation and Support of Sets / 2.4:
Convex Cones and Polarity / 2.5:
Polyhedral Sets, Extreme Points, and Extreme Directions / 2.6:
Linear Programming and the Simplex Method / 2.7:
Convex Functions and Generalizations. / Chapter 3:
Definitions and Basic Properties / 3.1:
Subgradients of Convex Functions / 3.2:
Differentiable Convex Functions / 3.3:
Minima and Maxima of Convex Functions / 3.4:
Generalizations of Convex Functions / 3.5:
Optimality Conditions and Duality. / Part 2:
The Fritz John and Karush-Kuhn-Tucker Optimality Conditions. / Chapter 4:
Unconstrained Problems / 4.1:
Problems Having Inequality Constraints / 4.2:
Problems Having Inequality and Equality Constraints / 4.3:
Second-Order Necessary and Sufficient Optimality Conditions for Constrained Problems / 4.4:
Constraint Qualifications. / Chapter 5:
Cone of Tangents / 5.1:
Other Constraint Qualifications / 5.2:
Lagrangian Duality and Saddle Point Optimality Conditions. / 5.3:
Lagrangian Dual Problem / 6.1:
Duality Theorems and Saddle Point Optimality Conditions / 6.2:
Properties of the Dual Function / 6.3:
Formulating and Solving the Dual Problem / 6.4:
Getting the Primal Solution / 6.5:
Linear and Quadratic Programs / 6.6:
Algorithms and Their Convergence / Part 3:
The Concept of an Algorithm. / Chapter 7:
Algorithms and Algorithmic Maps / 7.1:
Closed Maps and Convergence / 7.2:
Composition of Mappings / 7.3:
Comparison Among Algorithms / 7.4:
Unconstrained Optimization. / Chapter 8:
Line Search Without Using Derivatives / 8.1:
Line Search Using Derivatives / 8.2:
Some Practical Line Search Methods / 8.3:
Closedness of the Line Search Algorithmic Map / 8.4:
Multidimensional Search Without Using Derivatives / 8.5:
Multidimensional Search Using Derivatives / 8.6:
Modification of Newton's Method: Levenberg-Marquardt and Trust Region Methods / 8.7:
Methods Using Conjugate Directions: Quasi-Newton and Conjugate Gradient Methods / 8.8:
Subgradient Optimization Methods / 8.9:
Penalty and Barrier Functions. / Chapter 9:
Concept of Penalty Functions / 9.1:
Exterior Penalty Function Methods / 9.2:
Exact Absolute Value and Augmented Lagrangian Penalty Methods / 9.3:
Barrier Function Methods / 9.4:
Polynomial-Time Interior Point Algorithms for Linear Programming Based on a Barrier Function / 9.5:
Methods of Feasible Directions. / Chapter 10:
Method of Zoutendijk / 10.1:
Convergence Analysis of the Method of Zoutendijk / 10.2:
Successive Linear Programming Approach / 10.3:
Successive Quadratic Programming or Projected Lagrangian Approach / 10.4:
Gradient Projection Method of Rosen / 10.5:
Reduced Gradient Method of Wolfe and Generalized Reduced Gradient Method / 10.6:
Convex-Simplex Method of Zangwill / 10.7:
Effective First- and Second-Order Variants of the Reduced Gradient Method / 10.8:
Linear Complementary Problem, and Quadratic, Separable, Fractional, and Geometric Programming. / Chapter 11:
Linear Complementary Problem / 11.1:
Convex and Nonconvex Quadratic Programming: Global Optimization Approaches / 11.2:
Separable Programming / 11.3:
Linear Fractional Programming / 11.4:
Geometric Programming / 11.5:
Mathematical Review. / Appendix A:
Summary of Convexity, Optimality Conditions, and Duality. / Appendix B:
Bibliography.
Index
Introduction. / Chapter 1:
Problem Statement and Basic Definitions / 1.1:
Illustrative Examples / 1.2:
4.

Book

Book
[by] Edward J. Beltrami
Published: New York : Academic Press, 1970  xiv, 235 p. ; 24 cm
Series: Mathematics in science and engineering : a series of monographs and textbooks ; v. 63
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5.

Book

Book
Anthony L. Peressini, Francis E. Sullivan, J. Jerry Uhl, Jr.
Published: New York ; Tokyo : Springer-Verlag, c1988  x, 273 p. ; 25 cm
Series: Undergraduate texts in mathematics
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Table of Contents: Read more
Preface
Unconstrained Optimization via Calculus / 1:
Convex and Convex Functions / 2:
Iterative Methods for Unconstrained Optimization / 3:
Least Squares Optimization / 4:
Convex Programming and the Karush-Kuhn-Tucker Conditions / 5:
Penalty Methods / 6:
Optimization with Equality Constraints / 7:
Index
Preface
Unconstrained Optimization via Calculus / 1:
Convex and Convex Functions / 2:
6.

Book

Book
by Kenneth J. Arrow, Leonid Hurwicz, Hirofumi Uzawa ; with contributions by Hollis B. Chenery ... [et al.]
Published: Stanford, Calif. : Stanford University Press, 1958  229 p. ; 26 cm
Series: Stanford mathematical studies in the social sciences ; 2
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7.

Book

Book
editor, J. Abadie ; contributors, S. Vajda ... [et al.]
Published: Amsterdam : North-Holland, 1967  xxii, 316 p. ; 23 cm
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8.

Book

Book
[by] Anthony V. Fiacco [and] Garth P. McCormick
Published: New York : Wiley, c1968  xiv, 210 p. ; 23 cm
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9.

Book

Book
P.M. Pardalos, J.B. Rosen
Published: Berlin ; Tokyo : Springer-Verlag, c1987  vii, 143 p. ; 25 cm
Series: Lecture notes in computer science ; 268
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10.

Book

Book
Naum Z. Shor
Published: Boston : Kluwer, 1998  xvii, 394p ; 25cm
Series: Nonconvex optimization and its applications ; v. 24
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