Some convergent results for Backtracking Gradient Descent method on Banach spaces (2001.05768v2)

Abstract: Our main result concerns the following condition: {\bf Condition C.} Let $X$ be a Banach space. A $C^1$ function $f:X\rightarrow \mathbb{R}$ satisfies Condition C if whenever ${x_n}$ weakly converges to $x$ and $\lim {n\rightarrow\infty}||\nabla f(x_n)||=0$, then $\nabla f(x)=0$. We assume that there is given a canonical isomorphism between $X$ and its dual $X^*$, for example when $X$ is a Hilbert space. {\bf Theorem.} Let $X$ be a reflexive, complete Banach space and $f:X\rightarrow \mathbb{R}$ be a $C^2$ function which satisfies Condition C. Moreover, we assume that for every bounded set $S\subset X$, then $\sup _{x\in S}||\nabla ^2f(x)||<\infty$. We choose a random point $x_0\in X$ and construct by the Local Backtracking GD procedure (which depends on $3$ hyper-parameters $\alpha ,\beta ,\delta _0$, see later for details) the sequence $x{n+1}=x_n-\delta (x_n)\nabla f(x_n)$. Then we have: 1) Every cluster point of ${x_n}$, in the {\bf weak} topology, is a critical point of $f$. 2) Either $\lim {n\rightarrow\infty}f(x_n)=-\infty$ or $\lim _{n\rightarrow\infty}||x{n+1}-x_n||=0$. 3) Here we work with the weak topology. Let $\mathcal{C}$ be the set of critical points of $f$. Assume that $\mathcal{C}$ has a bounded component $A$. Let $\mathcal{B}$ be the set of cluster points of ${x_n}$. If $\mathcal{B}\cap A\not= \emptyset$, then $\mathcal{B}\subset A$ and $\mathcal{B}$ is connected. 4) Assume that $X$ is separable. Then for generic choices of $\alpha ,\beta ,\delta _0$ and the initial point $x_0$, if the sequence ${x_n}$ converges - in the {\bf weak} topology, then the limit point cannot be a saddle point.