Pages that link to "Item:Q359630"

The following pages link to Gradient methods for minimizing composite functions (Q359630):

Displaying 50 items.

• A proximal difference-of-convex algorithm with extrapolation (Q1744881) (← links)
• Proximal quasi-Newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates (Q1744900) (← links)
• Universal method for stochastic composite optimization problems (Q1746349) (← links)
• Pathwise coordinate optimization for sparse learning: algorithm and theory (Q1747736) (← links)
• Accelerating the DC algorithm for smooth functions (Q1749446) (← links)
• DC formulations and algorithms for sparse optimization problems (Q1749449) (← links)
• Nesterov's smoothing technique and minimizing differences of convex functions for hierarchical clustering (Q1749775) (← links)
• I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error (Q1750288) (← links)
• Solving structured nonsmooth convex optimization with complexity \(\mathcal {O}(\varepsilon ^{-1/2})\) (Q1752352) (← links)
• Accelerated primal-dual proximal block coordinate updating methods for constrained convex optimization (Q1753069) (← links)
• An optimal randomized incremental gradient method (Q1785198) (← links)
• Complexity bounds for primal-dual methods minimizing the model of objective function (Q1785201) (← links)
• Reconstruction of 3D X-ray CT images from reduced sampling by a scaled gradient projection algorithm (Q1790679) (← links)
• Iterative parametric minimax method for a class of composite optimization problems (Q1916766) (← links)
• An alternating direction method of multipliers with the BFGS update for structured convex quadratic optimization (Q1983931) (← links)
• On the quality of first-order approximation of functions with Hölder continuous gradient (Q1985266) (← links)
• The landscape of empirical risk for nonconvex losses (Q1991675) (← links)
• Inexact proximal \(\epsilon\)-subgradient methods for composite convex optimization problems (Q2010107) (← links)
• Second-order orthant-based methods with enriched Hessian information for sparse \(\ell _1\)-optimization (Q2013139) (← links)
• Local and global convergence of a general inertial proximal splitting scheme for minimizing composite functions (Q2013141) (← links)
• Point process estimation with Mirror Prox algorithms (Q2019904) (← links)
• Linear convergence of inexact descent method and inexact proximal gradient algorithms for lower-order regularization problems (Q2022292) (← links)
• Inertial proximal gradient methods with Bregman regularization for a class of nonconvex optimization problems (Q2022322) (← links)
• Globalized inexact proximal Newton-type methods for nonconvex composite functions (Q2028488) (← links)
• Nearly optimal first-order methods for convex optimization under gradient norm measure: an adaptive regularization approach (Q2031939) (← links)
• The condition number of a function relative to a set (Q2039239) (← links)
• Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems (Q2042418) (← links)
• Self adaptive inertial extragradient algorithms for solving bilevel pseudomonotone variational inequality problems (Q2044141) (← links)
• Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences (Q2044479) (← links)
• Accelerated Bregman proximal gradient methods for relatively smooth convex optimization (Q2044481) (← links)
• A FISTA-type accelerated gradient algorithm for solving smooth nonconvex composite optimization problems (Q2044494) (← links)
• Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization (Q2044495) (← links)
• Stochastic proximal splitting algorithm for composite minimization (Q2047212) (← links)
• Iteration complexity of generalized complementarity problems (Q2067800) (← links)
• A piecewise conservative method for unconstrained convex optimization (Q2070340) (← links)
• Mining events with declassified diplomatic documents (Q2078743) (← links)
• High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data (Q2079618) (← links)
• Generalized Nesterov's accelerated proximal gradient algorithms with convergence rate of order \(o(1/k^2)\) (Q2082553) (← links)
• Some modified fast iterative shrinkage thresholding algorithms with a new adaptive non-monotone stepsize strategy for nonsmooth and convex minimization problems (Q2082554) (← links)
• Inertial proximal incremental aggregated gradient method with linear convergence guarantees (Q2084299) (← links)
• Linesearch Newton-CG methods for convex optimization with noise (Q2084588) (← links)
• Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization (Q2088969) (← links)
• From differential equation solvers to accelerated first-order methods for convex optimization (Q2089788) (← links)
• A control-theoretic perspective on optimal high-order optimization (Q2089793) (← links)
• Nonregular and minimax estimation of individualized thresholds in high dimension with binary responses (Q2091840) (← links)
• A relaxed parameter condition for the primal-dual hybrid gradient method for saddle-point problem (Q2097452) (← links)
• Sparse regression at scale: branch-and-bound rooted in first-order optimization (Q2097642) (← links)
• On stochastic accelerated gradient with convergence rate (Q2111814) (← links)
• Accelerated proximal envelopes: application to componentwise methods (Q2116598) (← links)
• Additive Schwarz methods for convex optimization with backtracking (Q2122660) (← links)