Diehl, Fast inexact decomposition algorithms for large-scale separable convex optimization, Optimization 65 (2016), no. 2, 325-356.Q. Tran-Dinh, I. Necoara, C. Savorgnan, and M. Diehl, "An Inexact Perturbed Path
Accelerated first-order methods for large-scale convex optimization: nearly optimal complexity under strong convexityStructured nonsmooth convex optimizationFirst-order black-box oracleEstimation sequenceStrong convexityOptimal complexityWe introduce four accelerated (sub)gradient algorithms (ASGA) for solving ...
Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints Anastasia Bayandina, Pavel Dvurechensky, Alexander Gasnikov, Fedor Stonyakin, Alexander Titov Pages 181-213 Download chapter PDF Frank-Wolfe Style Algorithms for Large Scale Optimization Lijun Ding, Madeleine Ude...
In this paper, motivated by recently developed stochastic block coordinate algorithms, we propose a highly scalable randomized block coordinate Frank-Wolfe algorithm for convex optimization with general compact convex constraints, which has diverse applications in analyzing biomedical data for better ...
Multi-agent distributed optimization has drawn significant attention in recent years due to the need for large-scale signal processing and machine learning tasks over networks. Distributed optimization methods are appealing as it is not always efficient to pool all the local information for centralized ...
Implementation of mini-batch optimization algorithms for ODE models We assume the time evolution of a vector of state variablesx(t) to be given by the ODE system $$\dot{x}=f(t,x(t,\theta ,{u}^{e}),\theta ,{u}^{e}),\quad x(0)={x}_{0}(\theta ,{u}^{e}),$$ ...
Another notable existing large-scale tool for distributed implementations is MapReduce [15] and its open source implementation, Hadoop [16]. However, MapReduce was designed for parallel processing and it is ill-suited for the iterative computations inherent in optimization algorithms [4,17]. HPCC ...
In general, SNOPT requires less matrix computation than NPSOL and fewer evaluations of the functions than the nonlinear algorithms in MINOS [19, 1]. It is suitable for nonlinear problems with thousands of constraints and variables, and is efficient if many constraints and bounds are active at a...
Esser, E., Zhang, X., Chan, T.F.: A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. SIAM J. Imag. Sci.3(4), 1015–1046 (2010) ArticleMathSciNetMATHGoogle Scholar
We demonstrate that the algorithms can supply a high quality solution efficiently even for some ill-conditioned problems.doi:10.1007/s10589-015-9812-yWangChengjingKluwer Academic PublishersComputational Optimization & ApplicationsC. Wang. On how to solve large-scale log-determinant optimization problems....