Unconstrained multidimensional

Conceptually, methods of feasible directions operate in a manner similar to unconstrained multidimensional search techniques. That is, the basic idea is, given a feasible point Xj, determine a direction dj and a step length Oj that yield the new point Xj+, = Xj + a dj. In the constrained case, however, care must to taken to choose a direction dj that not only produces a point Xj+, that improves the objective function, but also maintains the feasibility of Xj+,. For a differentiable objective function /(x), an improving feasible direction dj at the point Xj 6 S, has the following two properties (see Figure 13) ... 

The BzzMinimizationQuasiNewton class is designed to solve unconstrained multidimensional minimization problems using the quasi-Newton method as the principal algorithm. If the algorithm does not converge even though... 

Carlo run constrained to make moves on the 3N-1 dimensional dividing surface in configuration space, and supplying momenta from the appropriate multidimensional Maxwell distribution. Alternatively, the dividing surface may be sampled by an unconstrained Monte Carlo run that is encouraged to remain near S by adding to the potential a holding term that is constant on S but increases rapidly as moves away from S. 

Unconstrained optimization (nonlinear programming), 2546-2553 classictil methods, 2546-2547 conjugate gradient methods, 2552-2553 golden section method, 2547-2549 line search techniques for, 2547 multidimensional search techniques for, 2549-2552... 

Several authors proposed a series of tests for multidimensional unconstrained... 

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