 # Steepest ascent

(descent, if minimizing). This is a class of algorithms, where $LaTeX: \textstyle x' = x + sd,$ such that the direction vector d is chosen by maximizing the initial "velocity" of change, and the step size $LaTeX: (s)$ is chosen by line search. Generally used in the context of unconstrained optimization, the mathematical program is $LaTeX: \textstyle \max \{f(x): x \in R^n\},$ where $LaTeX: \textstyle f \in C^1.$ (For descent, change Max to Min.) Then, $LaTeX: d$ is chosen to maximize the first-order Taylor approximation, subject to a normalization constraint: $LaTeX: \textstyle \max \{ \nabla f(x) d: ||d|| = 1 \},$ where $LaTeX: ||d||$ denotes the norm of the direction vector, $LaTeX: d$. When the Euclidean norm is used, this yields the original steepest ascent algorithm by Cauchy, which moves in the direction of the gradient: $LaTeX: d = \nabla f(x) / ||\nabla f(x)||.$
(No direction vector is sought if $LaTeX: \textstyle \nabla f(x)=0;$ such algorithms stop when reaching a stationary point.)
Other norms, such as $LaTeX: \textstyle ||d||^2 = d' Q d,$ where $LaTeX: Q$ is symmetric and positive definite, lead to other directions that are steepest relative to that norm. In particular, if $LaTeX: \textstyle Q = H_f(x),$ this yields the modified Newton method.