Algorithms
omeco provides four optimization algorithms with different speed-quality trade-offs.
Algorithm Comparison
| Algorithm | Speed | Quality | Use Case |
|---|---|---|---|
| GreedyMethod | Fast (seconds) | Good | Quick optimization, large networks |
| ExhaustiveSearch | Exponential | Optimal | Small networks, exact baselines |
| TreeSA | Slower (minutes) | Better | High-quality solutions, important workloads |
| Treewidth | Fast (ms) | Best on structured nets | Large graphical-model / relational networks |
Quick Guide
Use GreedyMethod when:
- You need results quickly
- Network has <100 tensors
- Greedy result is good enough
Use TreeSA when:
- You have time to optimize
- Need best possible solution
- Greedy result is too slow/large
- Working with complex tensor networks
Use ExhaustiveSearch when:
- The network is small enough for exact dynamic programming
- You need a known-optimal FLOP-count baseline
- You are testing or benchmarking heuristic optimizers
Use Treewidth when:
- The network is large and structured (graphical models, factor graphs, relational instances)
- A low-treewidth elimination order is likely to exist
- You need a fast, deterministic, high-quality order in milliseconds
Topics
- Greedy Method - Fast O(n² log n) optimization
- Exhaustive Search - Exact dynamic programming for small networks
- TreeSA - Simulated annealing for quality
- Treewidth - Scalable elimination-order heuristic
- Algorithm Comparison - Detailed benchmarks
Next Steps
Choose an algorithm to learn more, or see the comparison for benchmarks.