Even after optimizing your algorithm, wrong answers often arise due to overlooked edge cases or incorrect assumptions about the problem. For instance, a solution might work well on typical inputs but fail when given the maximum or minimum input sizes or special cases like all zeros or negative values. Thoroughly testing with corner cases and stress testing with random large inputs can help catch these issues. Another possible cause is misinterpreting the problem’s constraints or logic. Ensure that your algorithm not only optimizes for time but also covers every possible scenario, including edge cases. Debugging strategies such as testing with custom cases and comparing against a brute-force solution can help identify the issue.