So far, we have discussed the steepest descent and Newton’s method for optimization. We will conclude the discussion with a third method, which can also be seen as a member of the steepest descent family of methods. Instead of the Euclidean and quadratic norms, let us consider the following minimization task for obtaining the normalized descent direction,

$\begin{array}{ll}\hfill \mathit{v}& =arg\underset{\mathit{z}}{min}{\mathit{z}}^{\text{T}}\nabla J,\hfill \end{array}$

(6.57)

$\begin{array}{ll}\hfill \text{s.t.}& ||\mathit{z}|{|}_{1}=1,\hfill \end{array}$

(6.58)

where ||⋅||_{1} denotes the ℓ_{1} norm, defined as

$||z|{|}_{1}:=\sum _{i=1}^{l}|{z}_{i}|.$

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