TETRICS
  • Welcome
  • Introduction
    • TETRICS protocol
    • Intents
    • Solvers
      • Advanced Optimization Algorithms
      • Role of Searchers & Resolvers
      • Computational Complexity & Real-Time Data Integration
    • Privacy Preservation
      • Sealed-Bid Batch Auction
    • MEV (Maximal Extractable Value)
      • Advanced Strategies for Mitigating MEV
      • Comparative Analysis with Existing MEV Blockers
    • References
  • How it work
    • Order Flow
      • Declarative Input to Atomic On‑Chain Execution
  • Adapter Framework
  • Strategies
    • Overview
      • Vault Operation
      • Yield Farming Management
    • Ecosystem Strategies
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On this page
  • Overview
  • Benefits
  • Mechanism
  • Heuristic Search
  • Graph-based Matching
  1. Introduction
  2. Solvers

Advanced Optimization Algorithms

Overview

TETRICS'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: TETRICS integrates heuristic search techniques, including greedy algorithms and A* search, to navigate the solution space effectively.

Heuristic Search

To further refine the optimization process, TETRICS 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

TETRICS'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.

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Last updated 29 days ago