The optimization problem in this example comprises a linear objective function and linear constraints, hence linear programming is the best technique for solving it (refer to Chapter 7). [Pg.86]

By linear programming we mean a class of optimization methods able to solve problems with both linear objective function and linear constraints. [Pg.355]

In the BzzMath library, the Solve function that uses an object from the BzzMatrixSparseSymmetricLocked class as its first argument solves a linear system or the equivalent minimization of a quadratic function via the CG method. [Pg.165]

The linear variation method is widely used to find approximate molecular wave functions, and matrix algebra gives the most computationally efficient method to solve the equations of the linear variation method. If the functions fu. .., f in the linear variation function 4> = 2 =i c fit are made to be orthonormal, then = Jfffjdr = 8jj, and the homogeneous set of equations (8.55) for the coefficients that minimize the variational integral becomes [Pg.216]

Quantum chemists may use dozens, hundreds, thousands, or even millions of terms in linear variation functions so as to get accurate results for molecules. Obviously, a computer is essential for this work. The most efficient way to solve (8.59) (which is called the secular equation) and the associated linear equations (8.56) is by matrix methods (Section 8.6). [Pg.223]

In Figure 2. the model name is CASCADE, the type of solving procedure is LP since only linear constraints and a linear objective function appear. The objective function variable, Z, is minimized, and consequently, the solve statement is [Pg.953]

Chapter 1 presents some examples of the constraints that occur in optimization problems. Constraints are classified as being inequality constraints or equality constraints, and as linear or nonlinear. Chapter 7 described the simplex method for solving problems with linear objective functions subject to linear constraints. This chapter treats more difficult problems involving minimization (or maximization) of a nonlinear objective function subject to linear or nonlinear constraints [Pg.265]

The calculation of frequency-dependent linear-response properties may be an expensive task, since first-order response equations have to be solved for each considered frequency [1]. The cost may be reduced by introducing the Cauchy expansion in even powers of the frequency for the linear-response function [2], The expansion coefficients, or Cauchy moments [3], are frequency independent and need to be calculated only once for a given property. The Cauchy expansion is valid only for the frequencies below the first pole of the linear-response function. [Pg.11]

One special type of optimization problem involving restrictions or constraints has been solved quite successfully by a technique known as linear programming. From a mathematical viewpoint the basic form of the problem may be stated very briefly. Consider a linear response function of n variables [Pg.364]

We would like to stress the similarity between Eqs. (5) and (18). The main difference is that the poles of the linear response function are excitation energies rather than energy eigenvalues (c./. Eq. (11)) but in both cases, the residues correspond to transition moments between the ground state and excited states. The two-step procedure for evaluating the linear response function is now (c.f. Eqs. (6) and (7) ) solve [Pg.79]

The performed analysis of problems solved by using MEIS has shown the possibilities for their reduction to convex programming (CP) problems in many important cases. Such reduction is often associated with approximation of dependences among variables. There are cases of multivalued solutions to the formulated CP problems, when the linear objective function is parallel to one of the linear part of set D y). Naturally the problems with non-convex objective functions or non-convex attainability sets became irreducible to CP. Non-convexity of the latter can occur at setting kinetic constraints by a system of linear inequalities, p>art of which is specified not for the whole region D (y), but its individual zones. [Pg.50]

Actually, up to the present time, many-body relaxation is still an unsolved problem in condensed matter physics. In his magical year of 1905, Einstein solved the problem of diffusion of pollen particles in water discovered in 1827 by the botanist, Robert Brown. In this Brownian diffusion problem, the diffusing particles are far apart and do not interact with each other and the correlation function is the linear exponential function, exp(-t/r). It is by far simpler a problem than the interacting many-body relaxation/diffusion problem involved in glass transition. It is a pity that Einstein in 1905 was unaware of the experimental work of R. Kohlrausch and his intriguing stretch exponential relaxation function, exp[-(t/r) ], published in 1847 and followed by other publications by his son, F. Kohlrausch. [Pg.25]

The essential features of variationally-based approaches of MS-MA type are that the matrix elements contain the total Hamiltonian H instead of V (i.e. there is no separation into -h V) that the free-molecule product functions are replaced by antisymmetrized products [44b] that the expansions are truncated, and that the coefficients are obtained by treating the expansion as a linear variation function [31]. When the corresponding matrix equations are solved by partitioning or perturbation techniques [44b] the resultant first-order interaction resembles (3), while the second-order contribution resembles (12) except that the product functions are antisymmetrized and the summation is discrete and finite. The formal reduction of El and E2 for exact free-molecule functions is dealt with elsewhere [16, 18, 19], in the context of a second-order EN expansion. Except in cases of heavy overlap, the antisymmetrizer in the off-diagonal elements may safely be omitted, as E2 is already of second-order. [Pg.140]

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