matlab-performance-optimizer

# MATLAB Performance Optimizer

This skill provides comprehensive guidelines for optimizing MATLAB code performance. Apply vectorization techniques, memory optimization strategies, and profiling tools to make code faster and more efficient.

## 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-wise Operations

Use `.*`, `./`, `.^` for element-wise operations:

```matlab
% Instead of this:
for i = 1:length(x)
    y(i) = x(i)^2 + 2*x(i) + 1;
end

% Do this:
y = x.^2 + 2*x + 1;
```

### Logical Indexing

Replace conditional loops with logical indexing:

```matlab
% Instead of this:
count = 0;
for i = 1:length(data)
    if data(i) > threshold
        count = count + 1;
        fil