Gradient methods of maximization.
暂无分享,去创建一个
is an approximation to the point c. Assuming that x ( 0 ) is sufficiently close to c, we may obtain an improved approximation to c by considering the first few terms of the Taylor expansion of fix) about # ( 0 ) . Presumably, the greater the number of terms of the expansion that we consider, the better will be our improved approximation and the more rapidly will the corresponding iterative procedure converge. On the other hand, increasing the number of terms of the expansion involves the calculation of higher order derivatives and increases considerably the computational cost of each iteration.
[1] George E. Forsythe,et al. Solving linear algebraic equations can be interesting , 1953 .
[2] J. E. Kerrich. STATISTICAL INFERENCE IN DYNAMIC ECONOMIC MODELS , 1951 .
[3] H. B. Curry. The method of steepest descent for non-linear minimization problems , 1944 .
[4] G. Temple,et al. The General Theory of Relaxation Methods Applied to Linear Systems , 1939 .
[5] G. M.. Introduction to Higher Algebra , 1908, Nature.