Publication | Open Access
Tensor Methods for Minimizing Functions with H\"{o}lder Continuous Higher-Order Derivatives
14
Citations
12
References
2019
Year
Numerical AnalysisMathematical ProgrammingEngineeringVariational AnalysisPde-constrained OptimizationConvex OptimizationTensor SchemeUnconstrained MinimizationConvex FunctionsComputational ComplexityDerivative-free OptimizationInverse ProblemsComputer ScienceTensor MethodsFunctional AnalysisNondifferentiable OptimizationEnergy MinimizationApproximation Theory
In this paper we study $p$-order methods for unconstrained minimization of convex functions that are $p$-times differentiable with $\nu$-Holder continuous $p$th derivatives. We propose tensor schemes with and without acceleration. For the schemes without acceleration, we establish iteration complexity bounds of $\mathcal{O}\left(\epsilon^{-1/(p+\nu-1)}\right)$ for reducing the functional residual below a given $\epsilon\in (0,1)$. Assuming that $\nu$ is know, we obtain an improved complexity bound of $\mathcal{O}\left(\epsilon^{-1/(p+\nu)}\right)$ for the corresponding accelerated scheme. For the case in which $\nu$ is unknown, we present a universal accelerated tensor scheme with iteration complexity of $\mathcal{O}\left(\epsilon^{-p/[(p+1)(p+\nu-1)]}\right)$. A lower complexity bound of $\mathcal{O}\left(\epsilon^{-2/[3(p+\nu)-2]}\right)$ is also obtained for this problem class.
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