Documentation
README
MATLAB Performance Optimizer
Optimize MATLAB code performance with vectorization, memory management, and profiling tools.
When to Use This Skill
- Optimizing slow or inefficient MATLAB code
- Converting loops to vectorized operations
- Reducing memory usage
- Improving algorithm performance
- When user mentions: slow, performance, optimize, speed up, efficient, memory
- Profiling code to find bottlenecks
- Parallelizing computations
Core Optimization Principles
1. Vectorization (Most Important)
Replace loops with vectorized operations whenever possible.
SLOW - Using loops:
% Slow approach
n = 1000000;
result = zeros(n, 1);
for i = 1:n
result(i) = sin(i) * cos(i);
end
This is the opening of the README. Read the full README on GitHub.