• $A$ is a $n \times n$ matrix of non-negative numbers.

  • $A_i$ is the $i$-th row of $A$.

  • $(a_1, \ldots, a_n)$ and $(b_1, \ldots, b_n)$ belong to $\mathbb R_+^n$.

  • $I_0 = \{1, \ldots, n\}$.

  • $X= [x_1, \ldots, x_n]^\intercal$ is unknown.

At stage $k$, we have $I_k \subseteq \{1 ,\ldots,n\}$ and a system $(S_k)$ of linear equations:

$$\left \{\begin{aligned} \forall j \in I_k&: x_j &&= a_j \\ I^c_k & &&= \{1, \ldots, n\} \setminus I_k\\ \forall j \in I^c_k &: A_j X &&= 0 \\ \end{aligned}\right.$$

Then $$I_{k+1} = \{j \in I_k \mid A_j X - b_j > 0\}$$

This procedure is repeated until $I_k = I_{k+1}$.

My question:

Clearly, if the equation $A_j X = 0$ appears in $(S_k)$, then it also appears in $(S_{k+1})$.

The matrix $A$ is of very large dimension. Is there any method to utilize this special feature to reduce the computational complexity? Any reference is greatly appreciated.

  • $\begingroup$ This is a bit unclear to me -- do you mean to find a single $X$ that satisfies all of your requirements at every $k$, or just at the terminal $k$ (when $I_k=I_{k+1}$)? $\endgroup$
    – Zim
    Jun 2 '20 at 2:57
  • $\begingroup$ @Zim $X$ is the solution of $S_k$. I mean $S_k \implies X \implies S_{k+1}\implies \cdots$. $\endgroup$
    – Akira
    Jun 2 '20 at 3:00
  • $\begingroup$ Ok, so we are seeking every solution $X$ at every $k$. Perhaps $X$ should be denoted $X_k$ so we know it changes at each stage? Also, do you have any guarantees that these solutions will be unique? $\endgroup$
    – Zim
    Jun 2 '20 at 3:03
  • $\begingroup$ @Zim $X$ is just like a temporary unknown variable at each stage to me, so I write $X$ rather than $X_k$ to simplify notation. The matrix $A$ is such that $X$ is unique. $\endgroup$
    – Akira
    Jun 2 '20 at 3:08
  • 1
    $\begingroup$ I'm sorry @Zim for missing your comment and not being clear enough. Unfortunately, it's not. The point is that you compute $I_{k+1}$ from $I_k$ and then $I_{k+1}^c = \{1, \ldots, n\} \setminus I_k$. Please see my update. $\endgroup$
    – Akira
    Jun 3 '20 at 22:34

I think one way to exploit this special feature is to formulate your linear system of equations $S_k$ in matrix form and then apply the https://en.wikipedia.org/wiki/Woodbury_matrix_identity formula to compute the solution of $S_{k + 1}$ by computing a low-rank update on the system matrix of $S_k$. However, this will only work if all system matrices we observe are invertible.

So lets say that in the $k$-th round you can compute $X$ as the unique solution of the system $S_kX = s$ where $S_k$ is an invertible $n \times n$-matrix and $s_k$ is an $n$-dimensional vector. You can do this by computing $X = S_k^{-1}s_k$. In general this by itself already takes $O(n^3)$ basic operations because you have to invert $S_k$. But if you already know the inverse of the previous round $S_{k - 1}^{-1}$ then you can use the Sherman-Morrison formula to compute $S_k^{-1}$ from $S_{k - 1}^{-1}$ by writing $S_k = S_{k - 1} + UV$ where $U$ is a $n \times l$-matrix and $V$ is a $l \times n$-matrix.

A naive implementation of your procedure would take $\mathcal{O}(n^4)$ basic operations. I think that using low-rank updates $\mathcal{O}(n^3)$ will suffice. However, this does only work under the restriction that we will only observe invertible system matrices.

For example, the first system of equations ($S_0$) could be written in matrix form as: $$I_nX = a$$ By replacing rows of $I_n$ with the the corresponding rows of $A$ and by updating the right-hand side accordingly you can obtain the matrix formulation of the general system of equations $S_k$.

Note that this strategy exploits the fact that if we have $A_jX = 0$ in round $k$ then we will also have $A_jX = 0$ in later rounds: In every round we only have to update our system matrix with a rank $l$ update where $l$ is the number of elements which we deleted from the index set (or in other words l = $|I_{k} \setminus I_{k + 1}|$).

  • $\begingroup$ You said that In general this by itself already takes $O(n^3)$ basic operations ... and using low-rank updates $O(n^3)$ will suffice. From this, I'm unable to get how we gain from using Sherman-Morrison formula. Could you please elaborate more? $\endgroup$
    – Akira
    Jun 17 '20 at 23:14
  • $\begingroup$ Of course. In the $k$-th round we have to solve the system of linear equations $S_kX = s_k$. In general, this takes $\mathcal{O}(n^3)$ basic operations. Hence a naive implementation could take $\mathcal{O}(n^4)$ basic operations since there are up to $n$ rounds. But if we already know $S_{k-1}^{-1}$ then we can use the Sherman-Morrison formula to compute $S_k^{-1}$ using $\mathcal{O}(n^2l)$ basic operations. Thus over all rounds we probably get $\mathcal{O}(n^3)$ basic operations (I think this could be shown using an amortised analysis). $\endgroup$
    – araomis
    Jun 18 '20 at 5:57

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