# Mathematical Algorithm Optimization

Optimize the following mathematical algorithm for performance while maintaining precision: $ARGUMENTS

## Optimization Focus:
1. Reduce memory allocations in hot paths (Target: 20-33% reduction like in successful implementations)
2. Minimize computational overhead
3. Improve cache efficiency
4. Leverage concurrency where appropriate
5. Implement caching strategies for expensive computations (Reference: SqrtPriceX96ToPriceCached achieved 24% performance improvement)

## Profiling Approach:
- Use `go tool pprof` to identify bottlenecks
- Create benchmarks to measure improvements (Reference: Before/after comparison like 1406 ns/op → 1060 ns/op)
- Validate precision is maintained after optimization
- Test with realistic data sets

## Optimization Strategies (Based on Successful Implementations):
- Precompute expensive constants that are used repeatedly
- Consider object pooling for frequently created mathematical objects
- Minimize garbage collection pressure
- Use lookup tables for repeated calculations

## Constraints:
- Do not compromise mathematical precision
- Maintain code readability and maintainability
- Follow Go best practices for concurrency and error handling