Code Optimizer

## Optimization: [Brief Description]

### Before (Inefficient)
```[language]
[original code]

[optimized code]

### Step 4: Measure and Validate

Ensure optimization actually improves performance.

**Measurement Techniques:**

**Python:**
```python
import time
import memory_profiler

# Time measurement
start = time.time()
result = function()
elapsed = time.time() - start
print(f"Elapsed: {elapsed:.4f}s")

# Memory measurement
from memory_profiler import profile

@profile
def function():
    # Code to profile
    pass

// Time measurement
long start = System.nanoTime();
result = function();
long elapsed = System.nanoTime() - start;
System.out.println("Elapsed: " + elapsed / 1_000_000 + "ms");

// Memory measurement
Runtime runtime = Runtime.getRuntime();
long before = runtime.totalMemory() - runtime.freeMemory();
result = function();
long after = runtime.totalMemory() - runtime.freeMemory();
System.out.println("Memory used: " + (after - before) / 1024 + "KB");

# Before: O(n) with overhead
numbers = []
for i in range(1000):
    if i % 2 == 0:
        numbers.append(i * 2)

# After: O(n) faster execution
numbers = [i * 2 for i in range(1000) if i % 2 == 0]

# Gain: 2-3x faster

# Before: O(n) memory
def get_numbers(n):
    result = []
    for i in range(n):
        result.append(i ** 2)
    return result

numbers = get_numbers(1000000)  # Uses ~8MB memory

# After: O(1) memory
def get_numbers(n):
    for i in range(n):
        yield i ** 2

numbers = get_numbers(1000000)  # Uses minimal memory

# Gain: 99% less memory for large n

# Before: Slower
total = 0
for num in numbers:
    total += num

# After: Faster (C implementation)
total = sum(numbers)

# Gain: 10-20x faster for large lists

# Before: Repeated lookups
for i in range(len(data)):
    process(data[i])

# After: Single lookup
for item in data:
    process(item)

# Or with enumerate
for i, item in enumerate(data):
    process(item)

# Gain: Faster iteration, more Pythonic

# Before: O(n) per lookup
items = [1, 2, 3, 4, 5, ...]  # Large list
if x in items:  # O(n) lookup
    do_something()

# After: O(1) per lookup
items = {1, 2, 3, 4, 5, ...}  # Set
if x in items:  # O(1) lookup
    do_something()

# Gain: 100x faster for large collections

// Before: O(n²) - creates n strings
String result = "";
for (int i = 0; i < 1000; i++) {
    result += i + ",";  // Creates new string each time
}

// After: O(n) - single buffer
StringBuilder result = new StringBuilder();
for (int i = 0; i < 1000; i++) {
    result.append(i).append(",");
}
String output = result.toString();

// Gain: 100x faster for large loops

// Before: Wrong data structure
List<Integer> numbers = new ArrayList<>();
numbers.contains(42);  // O(n) lookup

// After: Right data structure
Set<Integer> numbers = new HashSet<>();
numbers.contains(42);  // O(1) lookup

// Gain: 1000x faster for large collections

// Before: Creates objects in loop
for (int i = 0; i < 1000; i++) {
    String key = new String("key" + i);  // Unnecessary
    map.put(key, value);
}

// After: Reuse or use literals
for (int i = 0; i < 1000; i++) {
    String key = "key" + i;  // String interning
    map.put(key, value);
}

// Gain: Less GC pressure, faster

// Before: Autoboxing overhead
List<Integer> numbers = new ArrayList<>();
for (int i = 0; i < 1000000; i++) {
    numbers.add(i);  // Boxing int to Integer
}

// After: Primitive arrays or specialized libraries
int[] numbers = new int[1000000];
for (int i = 0; i < 1000000; i++) {
    numbers[i] = i;  // No boxing
}

// Or use TIntArrayList from Trove
TIntArrayList numbers = new TIntArrayList();

// Gain: 50% less memory, faster access