Complementary composite minimization, small gradients in general norms, and applications

Cites Work

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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Gradient methods for minimizing composite functions
First-order methods of smooth convex optimization with inexact oracle
Fast first-order methods for composite convex optimization with backtracking
On lower complexity bounds for large-scale smooth convex optimization
Universal gradient methods for convex optimization problems
On general minimax theorems
On optimality of Krylov's information when solving linear operator equations
Information-based complexity of linear operator equations
Sharp uniform convexity and smoothness inequalities for trace norms
Asymptotic analysis of the exponential penalty trajectory in linear programming
Universal method for stochastic composite optimization problems
The convex geometry of linear inverse problems
A first-order primal-dual algorithm for convex problems with applications to imaging
Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions
Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria
On linear convergence of non-Euclidean gradient methods without strong convexity and Lipschitz gradient continuity
Mirror Prox algorithm for multi-term composite minimization and semi-separable problems
The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
Uniformly convex functions on Banach spaces
Optimal methods of smooth convex minimization
An unconstrained convex programming view of linear programming
First-Order Methods in Optimization
Relatively Smooth Convex Optimization by First-Order Methods, and Applications
The Approximate Duality Gap Technique: A Unified Theory of First-Order Methods
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Faster p-norm minimizing flows, via smoothed q-norm problems
Error Bounds, Quadratic Growth, and Linear Convergence of Proximal Methods
An homotopy method for l _p regression provably beyond self-concordance and in input-sparsity time
Regularization and Variable Selection Via the Elastic Net
Optimal Affine-Invariant Smooth Minimization Algorithms
On first-order algorithms forl₁/nuclear norm minimization
Convex Analysis
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Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems
On uniformly convex functions

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