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There is work on low-rank updates of matrix functions, for instance this one: Beckermann, Bernhard; Kressner, Daniel; Schweitzer, Marcel, Low-rank updates of matrix functions, SIAM J. Matrix Anal. App …

If I translate this problem into my language correctly, you have a Hermitian parameter-dependent matrix $A(t)$; you diagonalize various nearby samplings $A(t_1), A(t_2), A(t_3), \dots$ (so that the ma …

If you are familiar with einsum, maybe this explanation does it: axes[0] and axes[1] specify the locations of the repeated letters in the parameters of einsum. For instance, np.tensordot(a, b, axes= …

The non-negative rank of an entrywise non-negative matrix $A\in\mathbb{R}^{m\times n}_{\geq 0}$, i.e., the minimum $r$ for which a factorization $A = BC$ exists with $B\in\mathbb{R}^{m\times r}_{\geq …

I think I understand what you did, even without further explanations. At each time step $t_i$ you are solving in the least-squares sense a system of equations of the form $Y(t_i) = M(t_i) K$, with $M( …

The problem on which I originally made that comment is a linear algebra problem: consider the linear matrix equation $$ \sum_{i=1}^k A_i X B_i = C, $$ where $A_i,B_i,C \in \mathbb{R}^{n\times n}$ are …

A famous example is the boolean satisfiability problem (SAT). 2-SAT is not complicated to solve in polynomial time, but 3-SAT is NP-complete.