Proximal quasi-Newton for computationally intensive l 1-regularized M - estimators. In Advances in Neural Information Processing Systems.Kai Zhong , Ian E. H. Yen , Inderjit S. Dhillon , Pradeep Ravikumar, Proximal quasi-Newton for computationally intensive 1-regularized M-estimators, Proceedings of...
Wang, XiaoyuWang, XiaoYuan, Ya-xiangOptimization Methods and SoftwareWang, X.Y., Wang, X., Yuan, Y.X.: Stochastic proximal quasi-Newton methods for non-convex compos- ite optimization. Optimization Methods and Software pp. 1-27 (2018)...
In this paper, we propose a generic algorithmic framework for stochastic proximal quasi-Newton (SPQN) methods to solve non-convex composite optimization problems. Stochastic second-order information is explored to construct proximal subproblem. Under mild conditions we show the non-asympotic convergence...
Advances in numerical optimization have supported breakthroughs in several areas of signal processing. This paper focuses on the recent enhanced variants of the proximal gradient numerical optimization algorithm, which combine quasi-Newton methods with forward-adjoint oracles to tackle large-scale problems ...
CMA-ES keeps track of a covariance matrix to learn a second-order model of the underlying objective function similar to the approximation of the inverse Hessian matrix in the classical Quasi-Newton method [26, 27]. CMA-ES has been widely shown to be more effective than many first-order ...
Huang, W., Gallivan, K.A., Absil, P.-A.: A Broyden class of quasi-Newton methods for Riemannian optimization. SIAM J. Optim. 25(3), 1660–1685 (2015) Article MathSciNet Google Scholar Huang, W., Wei, K.: Extending FISTA to Riemannian optimization for sparse PCA (2019). arXiv...
Proximal quasi-Newton methods for nondifferentiable convex optimization - Chen, Fukushima - 1999 () Citation Context ...teps [Aus87], [Fuk84], [HUL93]. The last decade has produced a “new generation” of proximal bundle methods, designed to seek faster convergence; see [LS94], [LS96],...
Xiaojun Chen,Masao Fukushima. Proximal quasi-Newton methods for nondifferentiable convex optimization[J]. Mathematical Programming . 1999 (2)Xiaojun;Chen;Masao;Fukushima.Proximal quasi-Newton methods for nondifferentiable convex optimization.Mathematical Programming.1999...
Furthermore, we consider an accelerated\nvariant, based on FISTA [1], to the proximal quasi-Newton algorithm. A similar\naccelerated method has been considered in [7], where the convergence rate\nanalysis relies on very strong impractical assumptions. We present a modified\nanalysis while ...
44Citations 1Altmetric Explore all metrics Abstract This paper proposes an accelerated proximal point method for maximally monotone operators. The proof is computer-assisted via the performance estimation problem approach. The proximal point method includes various well-known convex optimization methods, suc...