High-dimensional optimization involves finding the optimal solution in spaces with a large number of variables, which can be computationally challenging due to the curse of dimensionality. Techniques such as dimensionality reduction, gradient-based methods, and heuristic algorithms are often employed to efficiently navigate and solve these complex problems.