# First-Order Dynamic Optimization for Streaming Convex Costs

arXiv: https://arxiv.org/abs/2310.07925 · HTML: https://mlsanity.com/paper/2310.07925/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [A Class of Prediction-Correction Methods for Time-Varying Convex Optimization](https://mlsanity.com/paper/1509.05196/) (2015)
- [Optimization and Learning with Information Streams: Time-varying Algorithms and Applications](https://mlsanity.com/paper/1910.08123/) (2019)
- [Distributed Convex Optimization for Continuous-Time Dynamics with Time-Varying Cost Function](https://mlsanity.com/paper/1507.04878/) (2015)
- [Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization](https://mlsanity.com/paper/1608.07544/) (2016)
- [Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents](https://mlsanity.com/paper/2210.14362/) (2022)
- [Contraction and Robustness of Continuous Time Primal-Dual Dynamics](https://mlsanity.com/paper/1803.05975/) (2018)
- [Distributed and time-varying primal-dual dynamics via contraction analysis](https://mlsanity.com/paper/2003.12665/) (2020)
- [tvopt: A Python Framework for Time-Varying Optimization](https://mlsanity.com/paper/2011.07119/) (2020)
- [Matching DNN Compression and Cooperative Training with Resources and Data Availability](https://mlsanity.com/paper/2212.02304/) (2022)
- [Escaping spurious local minimum trajectories in online time-varying nonconvex optimization](https://mlsanity.com/paper/1912.00561/) (2019)

## Built on by (most cited first, 2 of 2)

- [FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivative](https://mlsanity.com/paper/2512.20814/) (2025)
- [Time-Varying Convex Optimization with O(n) Computational Complexity](https://mlsanity.com/paper/2410.15009/) (2024)

## Abstract (arXiv)

This paper proposes a set of novel optimization algorithms for solving a class of convex optimization problems with time-varying streaming cost function. We develop an approach to track the optimal solution with a bounded error. Unlike the existing results, our algorithm is executed only by using the first-order derivatives of the cost function which makes it computationally efficient for optimization with time-varying cost function. We compare our algorithms to the gradient descent algorithm and show why gradient descent is not an effective solution for optimization problems with time-varying cost. Several examples including solving a model predictive control problem cast as a convex optimization problem with a streaming time-varying cost function demonstrate our results.