An inverse Hessian approximation is a method used to efficiently calculate the search direction in optimization algorithms like quasi-Newton methods, circumventing the direct computation of the Hessian matrix inverse, which is costly and complex. By iteratively updating the inverse Hessian approximation, algorithms can achieve faster convergence rates in large-scale optimization problems.