Conjecture on products of tilted stochastic matrices

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Let PP be a stochastic reversible matrix with stationary distribution muPmu_P, and let {ui}i=1n\{u_i\}_{i=1}^n be strictly positive vectors. For each ii, define the uiu_i-tilted matrix

D−1(Pui)PD(ui).D^{-1}(P u_i) P D(u_i).

Product conjecture. The product of the uiu_i-tilted matrices

∏i=1nD−1(Pui)PD(ui)\prod_{i=1}^n D^{-1}(P u_i) P D(u_i)

is reversible and has a stationary distribution that can be readily calculated using the vectors uiu_i and μP\mu_P.

The proposition established in the source proves the corresponding assertion for a product of two tilted matrices, including an explicit stationary distribution and positivity of the eigenvalues. The generalization to nn tilted matrices is conjectured but no formula or proof is supplied here.

References

Primary source

Assaf Hallak and Gal Dalal, “On the Products of Stochastic and Diagonal Matrices”, arXiv:2304.11634 (2023).

Progress summary

Refreshed
Claimed solved

A posted calculation claims the conjecture is false for products of three or more factors, but this alleged counterexample has not been independently verified.

Hallak and Dalal posed the conjecture in 2023: products of arbitrarily many tilted versions of one reversible stochastic matrix should remain reversible and have a computable stationary distribution.

Known results

  • One tilted matrix is reversible when PP is reversible, with stationary distribution proportional to u∘Pu∘μPu\circ Pu\circ\mu_P.
  • The product of two tilted matrices is reversible, with stationary distribution proportional to Pu∘μP∘vPu\circ\mu_P\circ v.
  • The two-factor product has real positive eigenvalues because it is similar to a positive-semidefinite matrix.
  • The paper gives an eigenvalue bound for arbitrary products, but not reversibility or a general stationary-distribution formula.

Posted attempt

A reader-written calculation claims an explicit strictly positive, symmetric 3×33\times3 example for which U2VU^2V is not reversible, and extends this to every length k≥3k\ge3 and dimension d≥3d\ge3. It therefore claims a complete disproof of the conjecture; the calculation has not been independently verified.

Current status (as of August 2026): The one- and two-factor cases are established, while a posted counterexample claims the conjecture fails for every k≥3k\ge3 but remains unverified.

Sources

Solutions 1

CounterexampleThis solution needs a summarySee full solutionHide full solution

A sharp counterexample to reversibility of products of tilted stochastic matrices

Problem: MathDB #358167, Conjecture 1 in Assaf Hallak and Gal Dalal, On the Products of Stochastic and Diagonal Matrices, arXiv:2304.11634, April 2023.

Conclusion: The conjecture is false for every product length at least three, even when the underlying stochastic matrix is symmetric, strictly positive, positive definite, and only three-dimensional. Both the number of factors and the dimension are minimal.

1. Source definitions and the established two-factor case

For a strictly positive vector uu and a row-stochastic matrix PP, write

TP(u)=D(Pu)−1PD(u),(1)T_P(u)=D(Pu)^{-1}P D(u), \tag{1}

where D(w)D(w) denotes the diagonal matrix with diagonal ww. Every row of TP(u)T_P(u) sums to one.

A row-stochastic matrix QQ with strictly positive stationary distribution π\pi is reversible when

πiQij=πjQjifor every i,j.(2)\pi_iQ_{ij}=\pi_jQ_{ji} \qquad\text{for every }i,j. \tag{2}

The source proves in Proposition 3 that TP(u)T_P(u) is reversible whenever PP is reversible. Its Proposition 5 further proves that

TP(u)TP(v)T_P(u)T_P(v)

is reversible for any two strictly positive tilt vectors u,vu,v. Conjecture 1 asserts that reversibility persists for products of arbitrarily many such factors. We disprove precisely this proposed extension; neither of the established one-factor or two-factor results is contradicted.

2. A strictly positive three-state counterexample

Take

P=14(211121112),u=(112),v=(212).(3)P= \frac14 \begin{pmatrix} 2&1&1\\ 1&2&1\\ 1&1&2 \end{pmatrix}, \qquad u= \begin{pmatrix}1\\1\\2\end{pmatrix}, \qquad v= \begin{pmatrix}2\\1\\2\end{pmatrix}. \tag{3}

Every entry of PP is strictly positive, its rows sum to one, and P=PTP=P^{\mathsf T}. It is therefore reversible with respect to

μP=(13,13,13).\mu_P=\left(\frac13,\frac13,\frac13\right).

In fact, its eigenvalues are 1,1/4,1/41,1/4,1/4, so it is also positive definite. The normalizing vectors are

Pu=(5/45/43/2),Pv=(7/43/27/4).Pu= \begin{pmatrix}5/4\\5/4\\3/2\end{pmatrix}, \qquad Pv= \begin{pmatrix}7/4\\3/2\\7/4\end{pmatrix}.

Thus the two tilted matrices are

U=TP(u)=(2/51/52/51/52/52/51/61/62/3),V=TP(v)=(4/71/72/71/31/31/32/71/74/7).(4)U=T_P(u)= \begin{pmatrix} 2/5&1/5&2/5\\ 1/5&2/5&2/5\\ 1/6&1/6&2/3 \end{pmatrix}, \qquad V=T_P(v)= \begin{pmatrix} 4/7&1/7&2/7\\ 1/3&1/3&1/3\\ 2/7&1/7&4/7 \end{pmatrix}. \tag{4}

Apply Conjecture 1 to the three strictly positive tilt vectors u,u,vu,u,v. Their product is

Q=U2V=(587/1575293/1575139/315572/157561/315698/1575673/1890173/945871/1890).(5)Q=U^2V= \begin{pmatrix} 587/1575&293/1575&139/315\\ 572/1575&61/315&698/1575\\ 673/1890&173/945&871/1890 \end{pmatrix}. \tag{5}

If a strictly positive stochastic matrix is reversible, multiplying the three detailed-balance equations around a directed three-cycle gives Kolmogorov's necessary cycle identity

Q12Q23Q31=Q13Q32Q21.(6)Q_{12}Q_{23}Q_{31} = Q_{13}Q_{32}Q_{21}. \tag{6}

But the product in (5) satisfies

Q12Q23Q31−Q13Q32Q21=688189612344190625−13754884468838125=1015315625>0.(7)\begin{aligned} Q_{12}Q_{23}Q_{31} -Q_{13}Q_{32}Q_{21} &= \frac{68818961}{2344190625} -\frac{13754884}{468838125}\\ &= \frac{101}{5315625} >0. \end{aligned} \tag{7}

Therefore QQ is not reversible. Its unique stationary distribution does exist and is

π=115931(5790,2965,7176),\pi= \frac1{15931}(5790,2965,7176),

but detailed balance fails explicitly:

π1Q12−π2Q21=14716895>0.(8)\pi_1Q_{12}-\pi_2Q_{21} =\frac{14}{716895}>0. \tag{8}

The obstruction is nonreversibility, not nonexistence or nonuniqueness of a stationary distribution.

3. Failure at every product length greater than two

The same fixed strictly positive matrices give counterexamples of every length. For k≥2k\ge2, put

Qk=Uk−1V.(9)Q_k=U^{k-1}V. \tag{9}

Set r=k−1r=k-1. An exact diagonalization of UU is

U=C(1/50004/150001)C−1,C=(−1−611−61051).(10)U=C \begin{pmatrix} 1/5&0&0\\ 0&4/15&0\\ 0&0&1 \end{pmatrix} C^{-1}, \qquad C= \begin{pmatrix} -1&-6&1\\ 1&-6&1\\ 0&5&1 \end{pmatrix}. \tag{10}

Define

z=5−r,w=(415)r.z=5^{-r}, \qquad w=\left(\frac4{15}\right)^r.

Substitution of

Qk=Cdiag⁡(z,w,1)C−1VQ_k=C\operatorname{diag}(z,w,1)C^{-1}V

into the cycle expression gives

(Qk)12(Qk)23(Qk)31−(Qk)13(Qk)32(Qk)21=F(z,w)1494108,(11)(Q_k)_{12}(Q_k)_{23}(Q_k)_{31} -(Q_k)_{13}(Q_k)_{32}(Q_k)_{21} =\frac{F(z,w)}{1494108}, \tag{11}

where

F(z,w)=−720w2z−726w2+440wz2+4223wz+2299w−2013z2−3503z.(12)F(z,w) =-720w^2z-726w^2+440wz^2+4223wz +2299w-2013z^2-3503z. \tag{12}

For k=2k=2, direct substitution gives

F(15,415)=0,F\left(\frac15,\frac4{15}\right)=0,

exactly as required by the source's two-factor theorem.

Suppose now that k≥3k\ge3, so r≥2r\ge2. Then

0<z≤125,0<w≤16225,zw=(34)r≤916.(13)0<z\le\frac1{25}, \qquad 0<w\le\frac{16}{225}, \qquad \frac zw=\left(\frac34\right)^r\le\frac9{16}. \tag{13}

Dividing (12) by ww and discarding its positive summands gives the uniform lower bound

F(z,w)w=2299−726w−720wz+440z2+4223z−zw(3503+2013z)≥2299−72616225−72016225125−916(3503+201325)=3443931500>0.(14)\begin{aligned} \frac{F(z,w)}w &= 2299-726w-720wz+440z^2+4223z -\frac zw\left(3503+2013z\right)\\ &\ge 2299-726\frac{16}{225} -720\frac{16}{225}\frac1{25} -\frac9{16}\left(3503+\frac{2013}{25}\right)\\ &= \frac{344393}{1500} >0. \end{aligned} \tag{14}

Hence the cycle imbalance in (11) is strictly positive for every k≥3k\ge3. The conjecture fails at every product length beyond the established two-factor threshold.

4. Sharpness in matrix dimension and extension to every larger dimension

Every strictly positive two-state stochastic matrix has the form

R=(1−aab1−b),a,b>0,R= \begin{pmatrix} 1-a&a\\ b&1-b \end{pmatrix}, \qquad a,b>0,

and is reversible with stationary distribution

πR=1a+b(b,a).\pi_R=\frac1{a+b}(b,a).

Thus no counterexample exists in dimension one or two. By Propositions 3 and 5 of the source, no counterexample exists with one or two tilted factors in any dimension. The three-dimensional, three-factor example (3)–(7) is therefore minimal in both parameters.

For completeness, counterexamples also exist in every dimension d≥3d\ge3 and at every length k≥3k\ge3. When d>3d>3, start with

P0=P⊕Id−3,u0=(1,1,2,1,…,1)T,v0=(2,1,2,1,…,1)T.P_0=P\oplus I_{d-3}, \qquad u_0=(1,1,2,1,\ldots,1)^{\mathsf T}, \qquad v_0=(2,1,2,1,\ldots,1)^{\mathsf T}.

At ε=0\varepsilon=0, the first three states of the product

TP0(u0)k−1TP0(v0)T_{P_0}(u_0)^{k-1}T_{P_0}(v_0)

have exactly the strictly positive cycle imbalance in (11). Now perturb to

Pε=(1−ε)P0+εJdd,0<ε<1,(15)P_{\varepsilon} =(1-\varepsilon)P_0 +\varepsilon\frac{J_d}{d}, \qquad 0<\varepsilon<1, \tag{15}

where JdJ_d is the all-ones matrix. This matrix is symmetric, strictly positive, and stochastic. Every tilted entry and every cycle imbalance is continuous at ε=0\varepsilon=0, because all normalizing coordinates are strictly positive there. Therefore, for all sufficiently small positive rational ε\varepsilon, the same three-state cycle remains unbalanced.

Consequently, within the class of strictly positive reversible stochastic matrices, the universal reversibility assertion holds precisely when either the matrix dimension is at most two or the number of tilted factors is at most two. It fails for every pair of parameters

d≥3,k≥3.d\ge3, \qquad k\ge3.

This resolves the conjecture negatively while preserving, and sharply delimiting, the source's established one-factor and two-factor results.