The Primal-Dual method for Learning Augmented Algorithms

10/22/2020
by   Étienne Bamas, et al.
0

The extension of classical online algorithms when provided with predictions is a new and active research area. In this paper, we extend the primal-dual method for online algorithms in order to incorporate predictions that advise the online algorithm about the next action to take. We use this framework to obtain novel algorithms for a variety of online covering problems. We compare our algorithms to the cost of the true and predicted offline optimal solutions and show that these algorithms outperform any online algorithm when the prediction is accurate while maintaining good guarantees when the prediction is misleading.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/01/2021

Online Primal-Dual Algorithms with Predictions for Packing Problems

The domain of online algorithms with predictions has been extensively st...
10/23/2019

Large Scale Model Predictive Control with Neural Networks and Primal Active Sets

This work presents an explicit-implicit procedure that combines an offli...
07/20/2021

Faster Matchings via Learned Duals

A recent line of research investigates how algorithms can be augmented w...
11/03/2020

Robust Algorithms for Online Convex Problems via Primal-Dual

Primal-dual methods in online optimization give several of the state-of-...
09/21/2022

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming

Semidefinite programming (SDP) is a unifying framework that generalizes ...
12/25/2020

Extreme Flow Decomposition for Multi-Source Multicast with Intra-Session Network Coding

Network coding (NC), when combined with multipath routing, enables a lin...
03/08/2021

Online Directed Spanners and Steiner Forests

We present online algorithms for directed spanners and Steiner forests. ...