编程技术、软件应用与系统模拟(Programming, Applicaiton and Simulation) |
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座右铭:
只做有益人类的事
不做有害人类的事
只做有益人类的事
不做有害人类的事
Downhill Simplex Method in MultidimensionsThe downhill simplex mehtod is due to Nelder and Mead. The mothod requires only function evaluations, not derivatives. It is not very efficient in terms of the number of function evaluations that it requires. A simplex if the geometrical figure consisting, in N dimensions, of N+1 points (or vertices) and all their interconnecting line segments, polygonal faces, etc. In two dimensions, a simplex is a triangle. In three dimensions it is a tetrahedron, not necessarily the regular tetrahedron. In general we are only interested in simplexes that are nondegenerate, i.e., that enclose a finite inner N-dimensional volume. If any point of a nondegenerate simplex is taken as the origin, then the N other points define vector directions that span the N-dimensional vector space. In one-dimensional minimizations, it was possible to bracket a minimum, so that the success of a subsequent isolation was guaranteed. There is no analogous procedure in multidimensional space. For multidimensional minimization, the best we can do is give our algorithm a starting guess, that is, an N-vector of independent variables as the first point to try. The algorithm is then supposed to make iss own waydownhill through the unimaginable complexity of an N-dimensional topography until it encounters a (local, at least) minimum. The downhill simplex method must be started not just with a single point, but with N+1 points, defining an initial simplex. If you think of one of these points (it matters not which) as being your initial starting point P0, then you can take the other N points to be The downhill simplex method now takes a series of steps, most steps just moving the point of the simplex where the function if largest ("highest point") through the opposite face of the simplex to a lower point. These steps are called reflections, and they are constructed to conserve the volume of the simplex (hence maintain its nondegeneracy). When it can do so, the method expands the simplex in one or another direction to take larger steps. When it reaches a valley floor, the method constracts itself in the transerve direction and tries to ooze down the valley. If there is a situation where the simplex itself in all directions, pulling itself in around its lowest (best) point. The routine name amoeba is intended to be descriptive of this kind of behavior. Termination criteria can be delicate in any multidimensional minimization routine. Without bracketing, and with more than one independent variable, we no longer have the option of requiring a certain tolerance for a single independent variable. We typically can identify one cycle or step of our multidimensional algorithms. It is then possible to terminate when the vector distance moved in that step is fractionally small in magnitude than some tolerance tol. Alternatively, we could require that the descrease in the function value in the terminating step be fractionally smaller than some tolerance ftol. Note that while tol should not usually be smaller than the square root of the machine precision, it is perfectly approciate to let ftol be of order the machine precision (or perhaps slightly larger so as not to be diddled br roundoff). Note well that either of hte above criteria might be fooled by a single anomalous step that, for one reason of another, failed to get anywhere. Therefore, it is frequently a good idea to restart a multidimensional minimization routine at a point where it claims to have found a minimum. For this restart, you should reinitialize any ancillary input quantities. In the downhill simplex method, for example, you should reinitialize N of N + 1 vertices of the simplex again by the above equation, with P0 being one of the vetices of the claimed minimum.
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