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Convex Optimization Basics
Convex Optimization Basics
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What does the gradient of a function f(x) represent geometrically?
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A.
The scalar value of the function evaluated at its global minimum point in the domain
B.
The direction of steepest ascent of f at a given point, with magnitude equal to the rate of increase
C.
The direction of steepest descent of f, pointing toward decreasing function values
D.
The curvature of the function at a given point, captured by the second-order Hessian
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