# A Distributed Optimization Algorithm over Time-Varying Graphs with Efficient Gradient Evaluations

## TL;DR

This paper introduces a distributed optimization algorithm for time-varying networks that balances communication and gradient evaluations to achieve fast convergence comparable to centralized methods.

## Contribution

It presents an optimized ratio of communication rounds to gradient evaluations, enabling efficient convergence in dynamic network settings.

## Key findings

- Achieves convergence rate similar to centralized gradient descent.
- Uses minimal communication rounds for convergence.
- Performs well on distributed target localization tasks.

## Abstract

We propose an algorithm for distributed optimization over time-varying communication networks. Our algorithm uses an optimized ratio between the number of rounds of communication and gradient evaluations to achieve fast convergence. The iterates converge to the global optimizer at the same rate as centralized gradient descent when measured in terms of the number of gradient evaluations while using the minimum number of communications to do so. Furthermore, the iterates converge at a near-optimal rate when measured in terms of the number of communication rounds. We compare our algorithm with several other known algorithms on a distributed target localization problem.