matlab-performance-optimizer

# 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:** ```matlab % Slow approach n = 1000000; result = zeros(n, 1); for i = 1:n result(i) = sin(i) * cos(i); end ``` **FAST - Vectorized:** ```matlab % Fast approach n = 1000000; i = (1:n).'; result = sin(i) .* cos(i); ``` ### 2. Preallocate Arrays **Always preallocate arrays before loops.** **SLOW - Growing arrays:** ```matlab % Very slow - array grows each iteration result = []; for i = 1:10000 result(end+1) = i^2; end ``` **FAST - Preallocated:** ```matlab % Fast - preallocated array n = 10000; result = zeros(n, 1); for i = 1:n result(i) = i^2; end ``` ### 3. Use Built-in Functions **MATLAB built-in functions are highly optimized.** **SLOW - Manual implementation:** ```matlab % Slow sum_val = 0; for i = 1:length(x) sum_val = sum_val + x(i); end ``` **FAST - Built-in function:** ```matlab % Fast sum_val = sum(x); ``` ## Vectorization Techniques ### Element-wi...