On the Fenchel Duality between Strong Convexity and Lipschitz Continuous Gradient
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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.
Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2018.
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Citing papers per year: 2021: 3; 2022: 12; 2023: 10; 2024: 11; 2025: 4; 2026: 2.
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- 2024An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation9 Dec 2024
- 2024Discrete distributions are learnable from metastable samples17 Oct 2024
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- 2024Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation2 May 2024
- 2024Speeding up and reducing memory usage for scientific machine learning via mixed precision30 Jan 2024
- 2024Point Cloud Classification via Deep Set Linearized Optimal Transport2 Jan 2024
- 2023Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global Insights12 Dec 2023
- 2023Convergence Analysis of Fractional Gradient Descent30 Nov 2023
- 2023Convex optimization over a probability simplex15 May 2023
- 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM25 Apr 2023
- 2023Learning Rate Schedules in the Presence of Distribution Shift27 Mar 2023
- 2023Online Learning for Equilibrium Pricing in Markets under Incomplete Information21 Mar 2023
- 2023Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets9 Mar 2023
- 2023Particle-based Online Bayesian Sampling28 Feb 2023
- 2023Robust Markov Decision Processes without Model Estimation2 Feb 2023
- 2023MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope8 Jan 2023
- 2022Covariance Estimators for the ROOT-SGD Algorithm in Online Learning2 Dec 2022
- 2022Private Blind Model Averaging - Distributed, Non-interactive, and Convergent3 Nov 2022
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directions24 Oct 2022
- 2022Distributed Distributionally Robust Optimization with Non-Convex Objectives14 Oct 2022
- 2022Wasserstein multivariate auto-regressive models for modeling distributional time series12 Jul 2022
- 2022Bregman Power k-Means for Clustering Exponential Family Data22 Jun 2022
- 2022Learning Energy Networks with Generalized Fenchel-Young Losses19 May 2022
- 2022Distributed Dynamic Safe Screening Algorithms for Sparse Regularization23 Apr 2022
- 2022Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents7 Apr 2022
- 2022Centered plug-in estimation of Wasserstein distances22 Mar 2022
- 2022Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning30 Jan 2022
- 2022A Simple Guard for Learned Optimizers28 Jan 2022
- 2021Spherical Matrix Factorization29 Nov 2021
- 2021Beyond Bandit Feedback in Online Multiclass Classification7 Jun 2021
- 2021The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication24 Mar 2021
- 2024Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulationcited by 21
- 2021Beyond Bandit Feedback in Online Multiclass Classificationcited by 14
- 2022Distributed Distributionally Robust Optimization with Non-Convex Objectivescited by 14
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2024Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptationcited by 12
- 2023Robust Markov Decision Processes without Model Estimationcited by 11
- 2022A Simple Guard for Learned Optimizerscited by 9
- 2022Learning Energy Networks with Generalized Fenchel-Young Lossescited by 8
- 2024Speeding up and reducing memory usage for scientific machine learning via mixed precisioncited by 8
- 2023Particle-based Online Bayesian Samplingcited by 7
- 2023Learning Rate Schedules in the Presence of Distribution Shiftcited by 7
- 2021The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communicationcited by 5
12 most cited of 40 All 40 (JSON)
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- 2025Improved Stochastic Optimization of LogSumExp29 Sep 2025
- 2025Normalized Iterative Hard Thresholding for Tensor Recovery6 Jul 2025
- 2025Towards Robust Learning to Optimize with Theoretical Guarantees17 Jun 2025
- 2024An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation9 Dec 2024
- 2024Discrete distributions are learnable from metastable samples17 Oct 2024
- 2024Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation10 Oct 2024
- 2024Extended convexity and smoothness and their applications in deep learning8 Oct 2024
- 2024Last-iterate Convergence Separation between Extra-gradient and Optimism in Constrained Periodic Games15 Jun 2024
- 2024MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations11 Jun 2024
- 2024Non-geodesically-convex optimization in the Wasserstein space1 Jun 2024
12 newest of 40 All 40 (JSON)