Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions
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The (1+1)-evolution strategy (ES) with success-based step-size adaptation is analyzed on a general convex quadratic function and its monotone transformation, that is, f(x) = g((x - x^*)^T H (x - x^*)), where g:ℝ→ℝ is a strictly increasing function, H is a positive-definite symmetric matrix, and x^* ∈ℝ^d is the optimal solution of f. The convergence rate, that is, the decrease rate of the distance from a search point m_t to the optimal solution x^*, is proven to be in O(exp( - L / Tr(H) )), where L is the smallest eigenvalue of H and Tr(H) is the trace of H. This result generalizes the known rate of O(exp(- 1/d )) for the case of H = I_d (I_d is the identity matrix of dimension d) and O(exp(- 1/ (d·ξ) )) for the case of H = diag(ξ· I_d/2, I_d/2). To the best of our knowledge, this is the first study in which the convergence rate of the (1+1)-ES is derived explicitly and rigorously on a general convex quadratic function, which depicts the impact of the distribution of the eigenvalues in the Hessian H on the optimization and not only the impact of the condition number of H.
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