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Chapter 3 Geometry of convex functions - Meboo Publishing ...

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3.7. GRADIENT 241<br />

2<br />

1.5<br />

1<br />

0.5<br />

Y 2<br />

0<br />

−0.5<br />

−1<br />

−1.5<br />

−2<br />

−2 −1.5 −1 −0.5 0 0.5 1 1.5 2<br />

Figure 74: Gradient in R 2 evaluated on grid over some open disc in domain<br />

<strong>of</strong> <strong>convex</strong> quadratic bowl f(Y )= Y T Y : R 2 → R illustrated in Figure 75.<br />

Circular contours are level sets; each defined by a constant function-value.<br />

Y 1<br />

3.7 Gradient<br />

Gradient ∇f <strong>of</strong> any differentiable multidimensional function f (formally<br />

defined inD.1) maps each entry f i to a space having the same dimension<br />

as the ambient space <strong>of</strong> its domain. Notation ∇f is shorthand for gradient<br />

∇ x f(x) <strong>of</strong> f with respect to x . ∇f(y) can mean ∇ y f(y) or gradient<br />

∇ x f(y) <strong>of</strong> f(x) with respect to x evaluated at y ; a distinction that should<br />

become clear from context.<br />

Gradient <strong>of</strong> a differentiable real function f(x) : R K →R with respect to<br />

its vector argument is defined<br />

⎡<br />

∇f(x) =<br />

⎢<br />

⎣<br />

∂f(x)<br />

∂x 1<br />

∂f(x)<br />

∂x 2<br />

.<br />

∂f(x)<br />

∂x K<br />

⎤<br />

⎥<br />

⎦ ∈ RK (1719)<br />

while the second-order gradient <strong>of</strong> the twice differentiable real function with

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