On the global optimization properties of finite-difference local descent algorithms
The global optimization problem (1) \(F(x)\to \inf_{x\in E_ k}\) with \(E_ k\) a \(k\)-dimensional Euclidean space is investigated for (1) \(\gamma\)-convex structured, that is there exists a strongly convex (with parameter \(\ell>0\)) and differentiable function \(\Phi(\cdot)\), \(\nabla\phi(\cdot)\) Lipschitzian with parameter \(L\), such that \(|\Phi(x)- F(x)|\leq \gamma\), \(\forall x\in E_ k\). The function \(\Phi(\cdot)\) is called strongly convex in \(E_ k\) with parameter \(\ell>0\), if \[ \Phi(\lambda x_ 1+ (1-\lambda)x_ 2)\leq \lambda \Phi(x_ 1)+ (1-\lambda)\Phi(x_ 2)- \ell\lambda(1-\lambda)\| x_ 1- x_ 2\|^ 2/2 \] for any \(x_ 1,x_ 2\in E_ k\) and \(\lambda\), \(0\leq \lambda\leq 1\). The author considers first a perturbed gradient descent method to approximating the global minimum of \(\Phi(x)\), then based on that introduces a finite difference algorithm aimed to solve approximately (1). Following the same approach he investigates the convergence properties of a coordinate descent method.
scientific article; zbMATH DE number 151887
scientific article