BOBYQA is Powell's trust-region method specifically designed for bound-constrained optimization. It builds quadratic models while respecting variable bounds, making it ideal for optimization problems with simple constraints like the unit hypercube [,]ⁿ used in our competition.
Interactive 3D Visualization
See BOBYQA in action on 3D optimization surfaces:
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Instructions: Choose a test function and algorithm, then click Start to watch the step-by-step optimization process.
Implementation Details
| Component | Details | Links |
|---|---|---|
| Original Algorithm |
M.J.D. Powell Trust-region method for bound-constrained problems Etends NEWUOA with eplicit bound handling Published: 29 |
📄 Paper |
| Reference Package |
PDO (Python Derivative-ree Optimization) Wraps Powell's original ortran BOBYQA implementation Handles bound constraints with numerical precision Package: pdfo |
📦 PDO Source |
| HumpDay Python Implementation |
Humpday Integration Calls PDO's BOBYQA implementation Standardized interface for optimization contests ile: humpday/optimizers/prima_algorithms.py |
Implementation |
| Humpday JavaScript Port |
Browser Implementation Bound-aware trust region implementation Optimized for unit hypercube constraints Class: PRIMA_BOBYQA |
JS Port |
🏁 Performance Characteristics
- Best for: Bound-constrained optimization problems, especially [,]ⁿ
- Dimensions: Ecellent performance up to + dimensions
- unction evaluations: Very efficient with bounds guidance
- Convergence: Superior to UOBYQA/NEWUOA on bounded problems
- Robustness: Handles boundary conditions elegantly