By Ya. Z. Tsypkin
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Extra info for Adaptation and Learning in Automatic Systems
The sequence of actions and operations which lead to the particular sought solution. The algorithmic methods are based on the numerical solutions of various equations, and now, in connection with a broad application of digital computers, they have become dominant. The algorithmic methods provide not only the solution, but also a way to find the solution on the basis of recursive formulas. Even in the cases when the analytic methods are applicable, it is sometimes preferable to use the algorithmic methods since they offer a faster and more convenient method of obtaining the sought result.
The direction of the gradient). Then, he has to move in that direction as long as it continues to descend. The points of rest are actually the local minima. , narrow passages, exist, the tpeleologist has to follow them until he reaches the lowest point. , when the speleologist encounters a rock, he has to move around it to the place where all directions lead to the top. Such behavior of the speleologist illustrates the basic idea of the gradient methods which is expressed in the algorithm of optimization.
All of these have rather complete bibliographies. One should not think that the problem of optimality is simple and clear. ” can cause despair and pessimism (see the papers by Zadeh (1958) and Kalman (1964)). The author, however, does not share such pessimism completely. 2 Here we use the notation adopted by Gantmacher (1959). All the vectors represent column matrices and the symbol c = ( c t , . . , cN). , cN]. The Bayesian criterion is broadly used in communication theory and radar engineering and recently in control theory (see the books by Gutkin (1961), Helstrom (1960) and Feldbaum (1965)).
Adaptation and Learning in Automatic Systems by Ya. Z. Tsypkin