Advanced Optimization Algorithms

Overview

GOEMON's system leverages heuristic methods to efficiently manage large sets of transaction intents in dynamic market conditions. These methods help streamline the process of finding near-optimal solutions quickly.

Benefits

• Quick Convergence: Heuristic methods allow the system to rapidly reach near-optimal solutions, even with many intents and changing market dynamics.

Mechanism

• Prioritization: Heuristics prioritize solutions that reduce gas use and execution latency, while maintaining strong MEV protection. They score execution paths based on cost, risk, and alignment with user-defined outcomes.

• Optimization: GOEMON integrates heuristic search techniques, including greedy algorithms and A* search, to navigate the solution space effectively.

Heuristic Search

To further refine the optimization process, GOEMON employs heuristic search techniques (such as greedy algorithms or A* search) to explore the solution space.

• Benefits: This approach reduces search complexity, identifies compatible intents efficiently, and streamlines execution batch formation.

• Example: When multiple users submit complementary intents (e.g., one to buy and another to sell at the same price), the solver applies graph-based matching to pair these intents efficiently, similar to solving a bipartite matching problem.

Graph-based Matching

GOEMON’s solver module represents transaction intents as nodes in a graph, with potential matches shown as edges. Using algorithms inspired by graph theory, such as maximum matching or minimum-cost flow, the solver identifies optimal pairs or groups of intents for execution. These graph theory techniques ensure efficient and effective matching, improving execution speed and

• Example: When multiple users submit intents with complementary conditions (e.g., one intent to buy and another to sell at the same price), the solver uses graph-based matching to quickly find and pair these intents, similar to solving a bipartite matching problem.

• Benefits: This approach reduces search complexity, efficiently identifies compatible intents, and streamlines the formation of execution batches.