Brute-force solutions fail when they have poor time complexity. Look for optimized algorithms that reduce unnecessary calculations.

Brute-force solutions involve trying every possible solution to a problem, which can quickly become impractical as the input size grows. For example, checking all subsets of an array or all possible permutations is typically inefficient, leading to time complexity of O(n!) or O(2^n), which is infeasible for large inputs. Instead of brute-forcing, you should look for optimized approaches, such as using dynamic programming, greedy algorithms, or divide-and-conquer techniques. Understanding the underlying patterns or properties of the problem can also lead to shortcuts that dramatically reduce the number of calculations needed. Efficient algorithms with better time complexities (like O(n log n) or O(n^2)) are often key to solving problems that brute-force solutions can't handle.