# How to Find a Matrix Closest to a Given One Under Certain Constraints

I was reading a paper about BFGS and met the following problem:

$\min_B \|B-B_k\|$, s.t. $B=B^{\top}, Bs_k=y_k, s_k^{\top}y_k>0$ and $B$ is positive definite. Here $B_k$ is a symmetric positive definite $n\times n$ matrix, $y_k,s_k$ are all $n\times 1$ vectors. $B_k, y_k,s_k$ are all known.

The norm used to measure the closeness between matrices is defined to be the weighted Frobenius norm, $\|A\|=\|W^{\frac{1}{2}}AW^{\frac{1}{2}}\|_F$.

The paper then gives the solution of this minimization problem as $B^* = (I-\rho_ky_ks_k^{\top})B_k(I-\rho_ks_ky_k^{\top})+\rho_ky_ky_k^{\top}$, where $\rho_k=\frac{1}{y_k^{\top}s_k}$, and calls it the Davidon-Fletcher-Powell formula.

I didn't have any clues about how to solve this optimization in closed form. Also, materials explaining the DFP formula seems to be ridiculously difficult to find. Could any one help?

Even any hints on solving matrix norm optimization problem would help, thanks!

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