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I have a set of matrix, which is:

  1. Real symmetric positive definite. Very sparse.
  2. Diagonal elements are positive while off-diagonal elements are negative.
  3. $a_{ii}=-\sum^{n}_{j=1}a_{ij}$ when $i\neq j$
  4. $a_{ii} \in (0,1]$
  5. $a_{ij} \in (-1,0]$ when $ i \neq j$

My experiments show that the largest eigenvalue of all the matrices I have are larger than 1. Can some one help me on proving that $ \lambda_{max} >1 $ for this matrix?

My first though is to prove that $Ax=\lambda x < x$ does not hold. But I couldn't get any break through. Thanks!

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Your conjecture can't be true unless you put some constraints on the diagonal entries or something. If you have a matrix $A$ that has eigenvalue $\lambda$, then the matrix $kA$ has eigenvalue $k\lambda$ with the same eigenvector space. And your matrix conditions are invariant under multiplying the matrix by a positive constant. So given a matrix that satisfies your constraints, you can find an arbitrarily small matrix with arbitrarily small eigenvalues that satisfies the same constraints.

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If there is always a '1' element in the diagonal element, can it be counted as the constraints you mentioned? Therefore the row the '1' appeared will be like: $x_{i} + \sum^{n}_{j=1} a_{ij}x_{j} < x_{i}$ for $Ax=\lambda_{max}x<x$. Thanks! – Jane Li May 1 '14 at 17:01

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