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- W2801722976 abstract "Over the last 40 years many powerful direct search algorithms have been developed for the unconstrained minimization of general functions. These algorithms require an initial estimate to the optimum point, denoted by (mathbf{x}^0). With this estimate as starting point, the algorithm generates a sequence of estimates (mathbf{x}^0), (mathbf{x}^1), (mathbf{x}^2), (dots ), by successively searching directly from each point in a direction of descent to determine the next point. The process is terminated if either no further progress is made, or if a point (mathbf{x}^k) is reached (for smooth functions) at which the first necessary condition in, i.e. ({varvec{nabla }} f(mathbf{x})=mathbf{0}) is sufficiently accurately satisfied, in which case (mathbf{x}^*cong mathbf{x}^k). It is usually, although not always, required that the function value at the new iterate (mathbf{x}^{i+1}) be lower than that at (mathbf{x}^i)." @default.
- W2801722976 created "2018-05-17" @default.
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- W2801722976 date "2018-01-01" @default.
- W2801722976 modified "2023-09-27" @default.
- W2801722976 title "LINE SEARCH DESCENT METHODS FOR UNCONSTRAINED MINIMIZATION" @default.
- W2801722976 doi "https://doi.org/10.1007/978-3-319-77586-9_2" @default.
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