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direction in which we seek for the solution should be _maximized_, with respect to derivative, to find minimum (otherwise we would often stand in the same place)(please, prove me if I'm wrong) |
forgot this in previous edit |
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:Maximize <math>\nabla^T f(x_k) \bar{x_k}</math>
:Subject to <math>\bar{x_k} \epsilon \mathbf{P}</math>
(note that this is a Linear Program. <math>x_k</math> is fixed during Step 3, while the
Step 4. Step size determination. Find <math>\lambda</math> that minimizes <math> f(x_k+\lambda(\bar{x_k}-x_k))</math> subject to <math>0 \le \lambda \le 1</math> . If <math>\lambda = 0</math> then Stop, we have found the minimum.
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