Linear Convergence on Positively Homogeneous Functions of a Comparison
Based Step-Size Adaptive Randomized Search: the (1+1) ES with Generalized
One-fifth Success Rule
In the context of unconstraint numerical optimization, this paper
investigates the global linear convergence of a simple probabilistic
derivative-free optimization algorithm (DFO). The algorithm samples a candidate
solution from a standard multivariate normal distribution scaled by a step-size
and centered in the current solution. This solution is accepted if it has a
better objective function value than the current one. Crucial to the algorithm
is the adaptation of the step-size that is done in order to maintain a certain
probability of success. The algorithm, already proposed in the 60's, is a
generalization of the well-known Rechenberg's (1+1) Evolution Strategy (ES)
with one-fifth success rule which was also proposed by Devroye under the name
compound random search or by Schumer and Steiglitz under the name step-size
adaptive random search. In addition to be derivative-free, the algorithm is
function-value-free: it exploits the objective function only through
comparisons. It belongs to the class of comparison-based step-size adaptive
randomized search (CB-SARS). For the convergence analysis, we follow the
methodology developed in a companion paper for investigating linear convergence
of CB-SARS: by exploiting invariance properties of the algorithm, we turn the
study of global linear convergence on scaling-invariant functions into the
study of the stability of an underlying normalized Markov chain (MC). We hence
prove global linear convergence by studying the stability (irreducibility,
recurrence, positivity, geometric ergodicity) of the normalized MC associated
to the (1+1)-ES. More precisely, we prove that starting from any initial
solution and any step-size, linear convergence with probability one and in
expectation occurs. Our proof holds on unimodal functions that are the
composite of strictly increasing functions by positively homogeneous functions
with degree α (assumed also to be continuously differentiable). This
function class includes composite of norm functions but also non-quasi convex
functions. Because of the composition by a strictly increasing function, it
includes non continuous functions. We find that a sufficient condition for
global linear convergence is the step-size increase on linear functions, a
condition typically satisfied for standard parameter choices. While introduced
more than 40 years ago, we provide here the first proof of global linear
convergence for the (1+1)-ES with generalized one-fifth success rule and the
first proof of linear convergence for a CB-SARS on such a class of functions
that includes non-quasi convex and non-continuous functions. Our proof also
holds on functions where linear convergence of some CB-SARS was previously
proven, namely convex-quadratic functions (including the well-know sphere
function)