## α-strong convexity, β-strong smoothness

Strong convexity often allows for optimization algorithms that converge very quickly to an $\epsilon$-optimum (rf. FISTA and NESTA). This post will cover some fundamentals of strongly convex functions.

## Convexity

For a convex function, $f: \mathbf{R} \to \mathbf{R}$ and $\forall \gamma \in [0, 1]$,

## Strict convexity

For a *strictly* convex function,

Geometrically, the definition of convexity implies that all points on any straight line connecting any two points in a convex set also lie in the set. Strict convexity excludes linear and affine functions, or functions with linear/affine subsets in their boundaries. (How would this extend to non-Euclidean geometries?)

## α-strong convexity

A function is α-strongly convex with respect to a norm |.| if, for α > 0

Alternatively,

or,

Strongly convexity extends strict convexity. For twice-differentiable functions, this implies that $\nabla^2f(x) \ge \alpha$. As Bubeck explains [1], strongly convex functions speed up convergence of first-order methods. Larger values of α imply larger gradient, and hence step size, when further away from the optimum.

Strong smoothness is another property of certain convex functions:

## $\beta$-smoothness

A function is $\beta$-smoothly convex with respect to a norm |.| if, for $\beta$ > 0, [4]

This definition gives an lower bound on the improvement in one step of (sub)gradient descent [1]:

Alternatively, $\beta$-smoothly convex function [lemma 3.3, 1]:

## Strong/smooth duality

Under certain conditions, a-strong convexity and $\beta$-smoothness are dual notions. For now, we’ll state the result without discussion.

If $f$ is a closed and convex function, then $f$ is $\alpha$-strongly convex function with respect to a norm $|.|$ if and only if $f^{\ast}$ is $\frac{1}{\alpha}$-strongly smooth with respect to the dual norm $|.|^{\ast}$ (corollary 7 in [4]).

## References:

- 1 S. Bubeck, Theory of Convex Optimization for Machine Learning, section 3.4
- 2 Wikipedia, Convex function
- 3 S. Boyd and G. Vandenberghe, Convex Optimization, section 9.1.2
- 4 S. M. Kakade, S. Shalev-Schwartz, A. Tiwari. On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization. Technical Report, 2009

## Credits

Image from Sebastian Pokutta’s post, Cheat Sheet: Smooth Convex Optimization.