Monotonicity of Lyapunov exponents

Let A be a square matrix with non-negative entries, such that the entries of A^TA are positive.
A nice example to start with is

    \[A=\begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} \,.\]

Given p \in     (0,1), consider a random matrix product

    \[Y_n=M_1\cdots M_n\]

where the M_k are independent, and
for each k,

    \[\mathbb P_p(M_k=A)=p \quad \text{and} \quad \mathbb P_p(M_k=A^T)=1-p \,.\]

Fix a
submultiplicative norm \| \cdot\| on d \times d matrices, e.g. the operator norm

    \[\|M\|=\sup_{\|x\|_2=1} \|Mx\|_2 \,,\]

where the supremum is over vectors x in the unit sphere of
{\mathbb R}^d. The sequence E_p(\|Y_n\|) is submultiplicative, i.e.

    \[\forall m,n \quad     E_p(\|Y_{m+n}\|) \le E_p(\|Y_m\|) \cdot E_p(\|Y_n\|)\,,\]

so by Fekete’s lemma, the limit

    \[L(p):=\lim_{n \to     \infty} \frac1n\log E_p(\|Y_n\|)\]

exists; it is called the top Lyapunov exponent for this matrix product, and was
first studied in [1]. In [2], it was shown that L(p) is a real analytic function of p.

  • (a) Does L(p) attain its maximum at p=1/2?

  • (b) Is L(p) nondecreasing in (0,1/2]?

[1] Furstenberg, Harry, and Harry Kesten. "Products of random
matrices." The Annals of Mathematical Statistics 31, no. 2 (1960):
457-469.

[2] Peres, Yuval Analytic dependence of Lyapunov exponents on
transition probabilities. Lyapunov exponents (Oberwolfach, 1990), 64–80,
Lecture Notes in Math., 1486, Springer, Berlin, 1991.

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