# A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization

arXiv: https://arxiv.org/abs/1507.06243 · HTML: https://mlsanity.com/paper/1507.06243/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2015.

## Built on by (most cited first, 8 of 8)

- [Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions](https://mlsanity.com/paper/2110.08150/) (2021)
- [Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums](https://mlsanity.com/paper/2102.13643/) (2021)
- [Zeroth and First Order Stochastic Frank-Wolfe Algorithms for Constrained Optimization](https://mlsanity.com/paper/2107.06534/) (2021)
- [On the Complexity of a Practical Primal-Dual Coordinate Method](https://mlsanity.com/paper/2201.07684/) (2022)
- [Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments](https://mlsanity.com/paper/2303.17192/) (2023)
- [First-Order Methods for Convex Optimization](https://mlsanity.com/paper/2101.00935/) (2021)
- [Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization](https://mlsanity.com/paper/2202.13212/) (2022)
- [Federated Frank-Wolfe Algorithm](https://mlsanity.com/paper/2408.10090/) (2024)

## Abstract (arXiv)

We propose a new first-order primal-dual optimization framework for a convex optimization template with broad applications. Our optimization algorithms feature optimal convergence guarantees under a variety of common structure assumptions on the problem template. Our analysis relies on a novel combination of three classic ideas applied to the primal-dual gap function: smoothing, acceleration, and homotopy. The algorithms due to the new approach achieve the best known convergence rate results, in particular when the template consists of only non-smooth functions. We also outline a restart strategy for the acceleration to significantly enhance the practical performance. We demonstrate relations with the augmented Lagrangian method and show how to exploit the strongly convex objectives with rigorous convergence rate guarantees. We provide numerical evidence with two examples and illustrate that the new methods can outperform the state-of-the-art, including Chambolle-Pock, and the alternating direction method-of-multipliers algorithms.