When implementing algorithms, prioritize efficiency in both time and space complexity. Consider these specific optimizations:

Use bitwise operations for mathematical computations when appropriate. For random number generation in ranges, bitwise operations can be more efficient than arithmetic expressions:
```
// Instead of: Math.floor(Math.random() * (65535 - 32768 + 1)) + 32768
// Use: Math.random() * 0x8000 | 0x8000
```
Prefer iterative solutions over recursive ones when the recursion depth could be problematic or when a simple loop would be more efficient and readable. Even if recursion is unlikely to cause issues, iterative approaches often have better performance characteristics and avoid potential stack overflow risks.
Understand memory implications of data structure operations. When working with TypedArrays, use subarray() to create views instead of slice() to create copies, unless you specifically need a copy. This avoids unnecessary memory allocation and copying:
```
// For views (no copy): array.subarray(start, end)
// For copies (when needed): array.slice(start, end)
```

These optimizations become particularly important in performance-critical code paths, large datasets, or resource-constrained environments.

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