PRIME: Efficient Algorithm for Token Graph Routing Problem

TL;DR

PRIME introduces a novel two-stage algorithm that significantly improves routing efficiency and execution prices in blockchain asset exchanges by combining pruned graph search with convex optimization.

Contribution

It presents PRIME, a new scalable algorithm for the Token Graph Routing Problem, combining graph pruning and convex optimization with a novel gradient method.

Findings

Outperforms industry baselines in Ethereum data experiments.

Achieves up to 8.42 bps better execution prices for large trades.

Reduces computation time by up to 96.7%.

Abstract

Optimizing asset exchanges on blockchain-driven platforms poses a novel and challenging graph query optimization problem. In this model, assets represent vertices and exchanges form edges, recasting the graph query task as a routing problem over a large-scale, dynamic graph. However, the existing solutions fail to solve the problem efficiently due to the non-linear nature of the edge weights defined by a concave swap function. To address the challenge, we propose PRIME, a two-stage iterative graph algorithm designed for the Token Graph Routing Problem (TGRP). The first stage employs a pruned graph search to efficiently identify a set of high-potential routing paths. The second stage formulates the allocation task as a strongly convex optimization problem, which we solve using our novel Adaptive Sign Gradient Method (ASGM) with a linear convergence rate. Extensive experiments on real-world Ethereum data confirm PRIME's advantages over industry baselines. PRIME consistently outperforms the widely-used Uniswap routing algorithm, achieving up to 8.42 basis points (bps) better execution prices on large trades while reducing computation up to 96.7%. The practicality of PRIME is further validated by its deployment in hedge fund production environments, demonstrating its viability as a scalable graph query processing solution for high-frequency decentralized markets.