# Online Optimization with Predictions and Non-convex Losses

@article{Lin2020OnlineOW, title={Online Optimization with Predictions and Non-convex Losses}, author={Yiheng Lin and Gautam Goel and Adam Wierman}, journal={Proceedings of the ACM on Measurement and Analysis of Computing Systems}, year={2020}, volume={4}, pages={1 - 32} }

We study online optimization in a setting where an online learner seeks to optimize a per-round hitting cost, which may be non-convex, while incurring a movement cost when changing actions between rounds. We ask:under what general conditions is it possible for an online learner to leverage predictions of future cost functions in order to achieve near-optimal costs? Prior work has provided near-optimal online algorithms for specific combinations of assumptions about hitting and switching costs… Expand

#### 10 Citations

Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms

- Computer Science, Engineering
- NeurIPS
- 2020

A gradient-based online algorithm, Receding Horizon Inexact Gradient (RHIG), is introduced, and its performance by dynamic regrets in terms of the temporal variation of the environment and the prediction errors is analyzed. Expand

Combining Regularization with Look-Ahead for Competitive Online Convex Optimization

- Computer Science
- IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
- 2021

This paper proposes a new algorithm, called Regularization with Look-Ahead (RLA), that can get the best of both AFHC and the regularization method, and provides a matching lower bound for the competitive ratios of all online algorithms with look-ahead. Expand

Beyond No-Regret: Competitive Control via Online Optimization with Memory

- Computer Science, Engineering
- ArXiv
- 2020

A novel reduction from online control of a class of controllable systems to online convex optimization with memory is provided and a new algorithm is designed that has a constant, dimension-free competitive ratio, leading to a new constant-competitive approach for online control. Expand

Information Aggregation for Constrained Online Control

- Computer Science
- Proc. ACM Meas. Anal. Comput. Syst.
- 2021

This paper uses a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC), and demonstrates that aggregated information can be efficiently learned using reinforcement learning algorithms. Expand

Dimension-Free Bounds on Chasing Convex Functions

- Computer Science, Mathematics
- COLT
- 2020

The problem of chasing convex functions, where functions arrive over time, is considered, and an algorithm is given that achieves an $O(\sqrt \kappa)$-competitiveness, when the functions are supported on $k$-dimensional affine subspaces. Expand

Optimization Algorithms as Robust Feedback Controllers

- Computer Science, Mathematics
- ArXiv
- 2021

This article reviews several research streams that have been pursued in this direction, including extremum seeking and pertinent methods from model predictive and process control, and focuses on recent methods under the name of “feedback-based optimization”, which directly implement optimization algorithms in closed loop with physical systems. Expand

Algorithms for Right-Sizing Heterogeneous Data Centers

- Computer Science
- SPAA
- 2021

An online algorithm based on a work function approach which achieves a competitive ratio of 2d + 1 + ε for any ε > 0.5 is developed, which is nearly optimal for time-independent operating cost functions. Expand

Competitive Control with Delayed Imperfect Information

- Computer Science, Mathematics
- ArXiv
- 2020

A greedy, myopic policy is introduced that yields a constant competitive ratio against the offline optimal policy with delayed feedback and inexact predictions, and the fundamental limits of online control with limited information are analyzed. Expand

Online Optimization with Memory and Competitive Control

- Computer Science
- NeurIPS
- 2020

The proposed approach, Optimistic Regularized Online Balanced Descent, achieves a constant, dimension-free competitive ratio and shows a connection between online optimization with memory and online control with adversarial disturbances. Expand

The Power of Predictions in Online Control

- Computer Science, Mathematics
- NeurIPS
- 2020

This analysis shows that the conventional greedy MPC approach is a near-optimal policy in both stochastic and adversarial settings, and requires only $O(\log T)$ predictions to reach dynamic regret, which matches the lower bound on the required prediction horizon for constant regret. Expand

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