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Program theories

Taha FLA (1975) Integer Programming Theory and Applications, Academic Press. [Pg.56]

Trochim W., 1989. Outcome pattern matching and program theory, Evaluation and Program Planning No. 12, pp. 355-366. [Pg.152]

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). [Pg.142]

Y. Nesterov and A. S. Nemirovskii, Interior Point Polynomial Method in Convex Programming Theory and Applications, SIAM, Philadelphia, 1993. [Pg.59]

H. Wolkowicz, R. Saigal, and L. Vandenberghe (eds.), Handbook of Semid nite Programming Theory, Algorithms, and Applications, Kluwer Academic Publishers, Nowell, MA, 2000. [Pg.91]

G. P. McCormick. Nonlinear Programming Theory, Algorithms and Applications. J. Wiley, New York, New York, 1983. [Pg.445]

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. [Pg.23]

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. [Pg.220]

TURTLE - A gradient VBSCF Program Theory and Studies of Aromaticity... [Pg.79]

M. Tawarmalani, V. Sahinidis, Global Optimization in Continuous and Mixed-Integer Nonlinear Programming Theory, Algorithms, Software, and Applications, Kluwer, Boston, 2001. [Pg.34]

Bazaraa, M.S. and C. M. Shetty. 1979. Nonlinear programming Theory and algorithms. New York, NY John Wiley and Sons, Inc. [Pg.200]

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. [Pg.322]

Duffin, R. J., Peterson, E. L., and Zener, C. (1963), Geometric Programming Theory and Application, John WUey Sons, New York. [Pg.2565]

Minoux, M., Mathematical Programming Theory and Algorithms, John Wiley Sons, New York, 1986. [Pg.2567]

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. ... [Pg.105]

Nesterov Y, Nemirovskii AS (1993) Interior point polynomial method in convex programming theory and applications. SIAM, Philadelphia... [Pg.173]

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... [Pg.818]

Integer Programming Theory and Practice John K. Karl of... [Pg.513]

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. [Pg.57]


See other pages where Program theories is mentioned: [Pg.60]    [Pg.663]    [Pg.1]    [Pg.9]    [Pg.610]    [Pg.622]    [Pg.2565]    [Pg.621]    [Pg.11]    [Pg.24]    [Pg.5]    [Pg.6]    [Pg.6]    [Pg.57]   


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