First-Order Dynamic Optimization for Streaming Convex Costs
Abstract
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.
Cite
@article{arxiv.2310.07925,
title = {First-Order Dynamic Optimization for Streaming Convex Costs},
author = {M. Rostami and H. Moradian and S. S. Kia},
journal= {arXiv preprint arXiv:2310.07925},
year = {2023}
}