# On minimizing matrix norm (AB-C)

Given A, B and C are matrices with dim(A) = m x n, dim(B) = n x p and dim (C) = m x p, the problem asks to evaluate

I need to learn $$\tilde{A}$$ such that $$\min_{\tilde{A}}||\tilde{A}^TB-C||$$

and $$\min_{\tilde{A}}||\tilde{A}-A||$$

In general, you can't. You can find a matrix that will minimize $||\tilde{A}^TB-C||$, and another that minimizes $||\tilde{A}-A||$, but, in general, the two matrices you find won't be the same. In fact, the matrix that solves the second problem is always $\tilde{A} = A$, and, if $||{A}^TB-C||$ is not the minimum of the first function, then your problem doesn't have a solution.
However, what would a machine learning practicioner do? Instead of finding a matrix that solves both of the problems (that might not exist), you can aim to solve a combination of the two problems. You can aim to find the matrix that minimizes $$||\tilde{A}^TB-C||^2 + ||\tilde{A}-A||^2$$ or, in general, the matrix that minimizes $$||\tilde{A}^TB-C||^2 + \lambda ||\tilde{A}-A||^2$$ for some $\lambda >0$. If $\lambda \approx 0$, then you are just solving the first problem, and if $\lambda >>0$, then you are just solving the second one.
• It is sufficient, but it is harder. If you use the $L^2$ norm, then solving a quadratic problem with a gradient method is very easy. If you don't use the $L^2$ norm, or you use the formula that you have given, then it becomes hell. I don't even know if there are global minima, and optimizing square roots is always difficult. – David Masip Jun 18 '18 at 13:52