Program theories

Taha FLA (1975) Integer Programming Theory and Applications, Academic Press. 

Trochim W., 1989. Outcome pattern matching and program theory, Evaluation and Program Planning No. 12, pp. 355-366. 

Avriel, M. Nonlinear Programming. Prentice-Hall, Englewood Cliffs, New Jersey (1976). Bazarra, M. S. H. D. Sherali and C. M. Shetty. Nonlinear Programming Theory and Algorithms. Wiley, New York (1993). 

Y. Nesterov and A. S. Nemirovskii, Interior Point Polynomial Method in Convex Programming Theory and Applications, SIAM, Philadelphia, 1993. 

H. Wolkowicz, R. Saigal, and L. Vandenberghe (eds.), Handbook of Semid nite Programming Theory, Algorithms, and Applications, Kluwer Academic Publishers, Nowell, MA, 2000. 

G. P. McCormick. Nonlinear Programming Theory, Algorithms and Applications. J. Wiley, New York, New York, 1983. 

J. H. van Lenthe, F. Dijkstra, W. A. Havenith, in Valence Bond Theory, D. L. Cooper, Ed., Elsevier, Amsterdam, The Netherlands, 2002, pp. 79-116. TURTLE—A Gradient VBSCF Program Theory and Studies of Aromaticity. 

Acknowledgements Financial support by the Deutsche Forschungsgemein-schaft within the program Theorie relativistischer Effekte in der Chemie und Physik schwerer Elemente (Project Dr 113/20-2) is gratefully acknowledged. P.B. was supported by the Austrian Science Foundation Project No P10847. 

TURTLE - A gradient VBSCF Program Theory and Studies of Aromaticity... 

M. Tawarmalani, V. Sahinidis, Global Optimization in Continuous and Mixed-Integer Nonlinear Programming Theory, Algorithms, Software, and Applications, Kluwer, Boston, 2001. 

Bazaraa, M.S. and C. M. Shetty. 1979. Nonlinear programming Theory and algorithms. New York, NY John Wiley and Sons, Inc. 

Tawarmalani M. and Sahinidis N.V. 2002. Convexification and global optimization in continuous and mixed-integer nonlinear programming Theory, algorithms, software, and apphcations. In, Nonconvex Optimization and Its Apphcations Series, Vol. 65. Kluwer Academic Publishers, Dordrecht. 

Duffin, R. J., Peterson, E. L., and Zener, C. (1963), Geometric Programming Theory and Application, John WUey Sons, New York. 

Minoux, M., Mathematical Programming Theory and Algorithms, John Wiley Sons, New York, 1986. 

The structure of the program follows (see Program 5, page 121). The subroutine JCOBI calculates roots and derivatives of the polynomial. The subroutine DFOPR calculates parameters Ai j and Bij associated with these roots. The subroutine FUN supplies information about the differential equations F is the vector on the right-hand side of Eq. (89). The subroutine OUT is the output subroutine. The latter two subroutines are supplied to DFOPR from the IMSL library, which solves a system of first-order differential equations with given initial conditions. The p optimization is included in this program theory behind P is detailed else-where. ... 

Nesterov Y, Nemirovskii AS (1993) Interior point polynomial method in convex programming theory and applications. SIAM, Philadelphia... 

Wolkowicz, H., Saigal, R Vandenberghe, L. / HANDBOOK OF SEMI-DEFINITE PROGRAMMING Theory, Algorithms, and Applications Hobbs, B.F. Meier, P. / ENERGY DECISIONS AND THE ENVIRONMENT A Guide to the Use of Multicriteria Methods... 

Integer Programming Theory and Practice John K. Karl of... 

The first part briefly introduces the stochastic expected value programming theory. With the discussing on the expected value model which is a convex programming, Theorem 4.2 is put forward and proved. Furthermore we get the conclusion that if the expected value model is a convex programming and there exists an optimal solution, then any local optimal solution will be the global optimal solution. 

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