# On the Fenchel Duality between Strong Convexity and Lipschitz Continuous Gradient

arXiv: https://arxiv.org/abs/1803.06573 · HTML: https://mlsanity.com/paper/1803.06573/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2018.

## Built on by (most cited first, 30 of 43)

- [Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation](https://mlsanity.com/paper/2405.01677/) (2024)
- [Beyond Bandit Feedback in Online Multiclass Classification](https://mlsanity.com/paper/2106.03596/) (2021)
- [Distributed Distributionally Robust Optimization with Non-Convex Objectives](https://mlsanity.com/paper/2210.07588/) (2022)
- [Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation](https://mlsanity.com/paper/2410.08305/) (2024)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Robust Markov Decision Processes without Model Estimation](https://mlsanity.com/paper/2302.01248/) (2023)
- [Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits](https://mlsanity.com/paper/2502.06051/) (2025)
- [A Simple Guard for Learned Optimizers](https://mlsanity.com/paper/2201.12426/) (2022)
- [Neural Approximate Mirror Maps for Constrained Diffusion Models](https://mlsanity.com/paper/2406.12816/) (2024)
- [Learning Energy Networks with Generalized Fenchel-Young Losses](https://mlsanity.com/paper/2205.09589/) (2022)
- [Speeding up and reducing memory usage for scientific machine learning via mixed precision](https://mlsanity.com/paper/2401.16645/) (2024)
- [Particle-based Online Bayesian Sampling](https://mlsanity.com/paper/2302.14796/) (2023)
- [Learning Rate Schedules in the Presence of Distribution Shift](https://mlsanity.com/paper/2303.15634/) (2023)
- [The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication](https://mlsanity.com/paper/2103.13026/) (2021)
- [Covariance Estimators for the ROOT-SGD Algorithm in Online Learning](https://mlsanity.com/paper/2212.01259/) (2022)
- [MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope](https://mlsanity.com/paper/2302.05294/) (2023)
- [Non-geodesically-convex optimization in the Wasserstein space](https://mlsanity.com/paper/2406.00502/) (2024)
- [MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations](https://mlsanity.com/paper/2406.07017/) (2024)
- [Improved Stochastic Optimization of LogSumExp](https://mlsanity.com/paper/2509.24894/) (2025)
- [Bregman Power k-Means for Clustering Exponential Family Data](https://mlsanity.com/paper/2206.10860/) (2022)
- [Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM](https://mlsanity.com/paper/2304.12534/) (2023)
- [Last-iterate Convergence Separation between Extra-gradient and Optimism in Constrained Periodic Games](https://mlsanity.com/paper/2406.10605/) (2024)
- [Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning](https://mlsanity.com/paper/2201.12718/) (2022)
- [Centered plug-in estimation of Wasserstein distances](https://mlsanity.com/paper/2203.11627/) (2022)
- [Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents](https://mlsanity.com/paper/2204.03187/) (2022)
- [Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets](https://mlsanity.com/paper/2303.05037/) (2023)
- [Discrete distributions are learnable from metastable samples](https://mlsanity.com/paper/2410.13800/) (2024)
- [An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation](https://mlsanity.com/paper/2412.06436/) (2024)
- [Convergence Rates for Distribution Matching with Sliced Optimal Transport](https://mlsanity.com/paper/2602.10691/) (2026)
- [Spherical Matrix Factorization](https://mlsanity.com/paper/2111.14536/) (2021)

## Abstract (arXiv)

We provide a simple proof for the Fenchel duality between strong convexity and Lipschitz continuous gradient. To this end, we first establish equivalent conditions of convexity for a general function that may not be differentiable. By utilizing these equivalent conditions, we can directly obtain equivalent conditions for strong convexity and Lipschitz continuous gradient. Based on these results, we can easily prove Fenchel duality. Beside this main result, we also identify several conditions that are implied by strong convexity or Lipschitz continuous gradient, but are not necessarily equivalent to them. This means that these conditions are more general than strong convexity or Lipschitz continuous gradient themselves.
