Recursive solutions, while elegant, can lead to inefficiency, especially if the same subproblems are solved multiple times. This inefficiency can often be fixed with memoization, where you store the results of subproblems and reuse them instead of recalculating. Another approach is to convert the recursion into an iterative solution, which eliminates the overhead of recursive calls and allows you to handle larger inputs. Dynamic programming (DP) can also help optimize recursive problems by storing results in a table. Tail recursion is another technique that can be optimized by some compilers or interpreters. Identifying and eliminating redundant calculations in recursive solutions will dramatically improve their performance in competitive programming.