# Time-Varying Convex Optimization: Time-Structured Algorithms and Applications

arXiv: https://arxiv.org/abs/2006.08500 · HTML: https://mlsanity.com/paper/2006.08500/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 30 of 31)

- [On Controller Tuning with Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2207.11120/) (2022)
- [Learning Time-Varying Graphs from Online Data](https://mlsanity.com/paper/2110.11017/) (2021)
- [Distributed support-vector-machine over dynamic balanced directed networks](https://mlsanity.com/paper/2104.00399/) (2021)
- [Event-Triggered Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2208.10790/) (2022)
- [Towards a Systems Theory of Algorithms](https://mlsanity.com/paper/2401.14029/) (2024)
- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)
- [Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics](https://mlsanity.com/paper/2503.07324/) (2025)
- [Convex Optimization with Nested Evolving Feasible Sets](https://mlsanity.com/paper/2605.07386/) (2026)
- [A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows](https://mlsanity.com/paper/2105.08832/) (2021)
- [Subgroup Fairness in Two-Sided Markets](https://mlsanity.com/paper/2106.02702/) (2021)
- [Robust Online Learning over Networks](https://mlsanity.com/paper/2309.00520/) (2023)
- [Time-Varying Optimization for Streaming Data Via Temporal Weighting](https://mlsanity.com/paper/2510.13052/) (2025)
- [Online Graph Learning in Dynamic Environments](https://mlsanity.com/paper/2110.05023/) (2021)
- [A Survey on Distributed Online Optimization and Game](https://mlsanity.com/paper/2205.00473/) (2022)
- [Maximum Likelihood With a Time Varying Parameter](https://mlsanity.com/paper/2302.14529/) (2023)
- [Online Distributed Learning with Quantized Finite-Time Coordination](https://mlsanity.com/paper/2307.06620/) (2023)
- [A Novel Theoretical Framework for Exponential Smoothing](https://mlsanity.com/paper/2403.04345/) (2024)
- [Prediction techniques for dynamic imaging with online primal-dual methods](https://mlsanity.com/paper/2405.02497/) (2024)
- [WEEP: A Differentiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope](https://mlsanity.com/paper/2507.20447/) (2025)
- [Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting](https://mlsanity.com/paper/2605.06971/) (2026)
- [Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systems](https://mlsanity.com/paper/2102.09111/) (2021)
- [Improving the Performance of Robust Control through Event-Triggered Learning](https://mlsanity.com/paper/2207.14252/) (2022)
- [Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications](https://mlsanity.com/paper/2306.05655/) (2023)
- [Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference](https://mlsanity.com/paper/2405.16029/) (2024)
- [Time-Varying Convex Optimization with O(n) Computational Complexity](https://mlsanity.com/paper/2410.15009/) (2024)
- [Online optimisation for dynamic electrical impedance tomography](https://mlsanity.com/paper/2412.12944/) (2024)
- [Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation](https://mlsanity.com/paper/2605.08864/) (2026)
- [A Controlled Diagnostic Study of Hardware-Induced Distortions in Hardware-Aware Training](https://mlsanity.com/paper/2605.09416/) (2026)
- [Distributed Optimization with Streaming Data: A Temporal Weighting Perspective](https://mlsanity.com/paper/2608.09565/) (2026)
- [Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions](https://mlsanity.com/paper/2609.11207/) (2026)

## Abstract (arXiv)

Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch algorithms for medium-scale problems to challenging dynamic, time-varying, and even huge-size settings. This is driven by technological transformations that converted infrastructural and social platforms into complex and dynamic networked systems with even pervasive sensing and computing capabilities. The present paper reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to both algorithmic development and performance analysis. It offers a comprehensive overview of available tools and methods, and unveils open challenges in application domains of broad interest. The real-world examples presented include smart power systems, robotics, machine learning, and data analytics, highlighting domain-specific issues and solutions. The ultimate goal is to exempify wide engineering relevance of analytical tools and pertinent theoretical foundations.