Solving linear equations can be reduced to a matrix-inversion problem, implying that the time complexity of the former problem is not greater than the time complexity of the latter. Conversely, given a solver of $N$ linear equations and $N$ unknown variables with computational cost $F(N)$, there is a trivial implementation of matrix inversion using the linear solver with overall computational cost equal to $N F(N)$.
However, the resulting algorithm is not optimal for matrix inversion. Indeed, the time complexity of linear solvers is not smaller than $N^2$, whereas the time complexity of matrix inversion is not bigger than $N^{2.375}$, as implied by the Coppersmith–Winograd algorithm.
Thus, my question is as follows. Given any solver of linear equations, is there some algorithm for inverting matrices that uses the linear solver and with the same time cost up to some constant? In other words, does a linear-solver with time cost $N^\alpha$ induce a matrix-inversion algorithm with cost $N^\alpha$? This question comes from the observation that the most efficient known linear solvers come from matrix-inversion algorithms. How is it possible that there is nothing more efficient than a mere factor improvement?
