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تسجيل مستخدم جديدAsymptotic behaviour of learning rates in Armijos condition
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Fix a constant $0<alpha <1$. For a $C^1$ function $f:mathbb{R}^krightarrow mathbb{R}$, a point $x$ and a positive number $delta >0$, we say that Armijos condition is satisfied if $f(x-delta abla f(x))-f(x)leq -alpha delta || abla f(x)||^2$. It is a basis for the well known Backtracking Gradient Descent (Backtracking GD) algorithm. Consider a sequence ${x_n}$ defined by $x_{n+1}=x_n-delta _n abla f(x_n)$, for positive numbers $delta _n$ for which Armijos condition is satisfied. We show that if ${x_n}$ converges to a non-degenerate critical point, then ${delta _n}$ must be bounded. Moreover this boundedness can be quantified in terms of the norms of the Hessian $ abla ^2f$ and its inverse at the limit point. This complements the first authors results on Unbounded Backtracking GD, and shows that in case of convergence to a non-degenerate critical point the behaviour of Unbounded Backtracking GD is not too different from that of usual Backtracking GD. On the other hand, in case of convergence to a degenerate critical point the behaviours can be very much different. We run some experiments to illustrate that both scenrios can really happen. In another part of the paper, we argue that Backtracking GD has the correct unit (according to a definition by Zeiler in his Adadeltas paper). The main point is that since learning rate in Backtracking GD is bound by Armijos condition, it is not unitless.
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