The symmetric group has elements, not the printed order . For the identity and the adjacent transpositions have probabilities , so the specified transition matrix is normalized. Its stationary distribution is uniform and it satisfies detailed balance, since each transposition is its own inverse. Adjacent transpositions generate the symmetric group, making this an irreducible Markov chain; the positive holding probability makes it an aperiodic Markov chain.
For a fixed label , right multiplication swaps positions, so is the label's position. Under the uniform distribution on a finite set it is uniform on . Therefore
For each generator , the squared change is one if the label occupies either of the two positions, and zero otherwise. Its stationary expected value is , giving
Thus the tagged-label Rayleigh quotient for adjacent transpositions gives the corrected bound
The printed factor six cannot be obtained: the hint's variance of a uniform distribution is wrong. Directly, . Bounded convergence in distribution of does imply convergence of its first two moments, hence convergence of its variance, but the limit is .
More strongly, the claimed asymptotic bound itself is false for this kernel. The Aldous spectral gap theorem states that the interchange process on a finite connected weighted graph has the same spectral gap as the single-label random walk with identical edge rates; see Theorem 1.1 and its proof. Here is that interchange process generator on a path graph with edge rates . For the tagged-label generator, direct substitution of gives eigenvalues , , including the reflecting endpoint equations. These distinct eigenvalues exhaust the tagged-label function space. Consequently the theorem yields
which exceeds asymptotically. This use of a substantial external theorem is only to certify the printed claim's failure; the corrected factor-twelve upper bound above follows directly from the requested test function and the variational formula.
The reference kernel printed in the hint has total mass , so it is not a Markov kernel. We explicitly replace it by the normalized random transposition shuffle: choose two positions independently and uniformly and swap them. Its probabilities are
These sum to one, and its ordinary spectral gap is , the value intended in the hint. This value can also be certified by the Aldous spectral gap theorem: the single-label generator has rate between distinct positions, so on centered functions it acts as multiplication by . Both shuffles have the same uniform distribution on a finite set as stationary distribution.
We use the Canonical paths comparison theorem in the following precise form. For two finite reversible Markov chains with the same positive stationary distribution , route each directed transition of along a positive-capacity path of . If
then and . Indeed, telescope each difference along its path, apply the Cauchy-Schwarz inequality, and sum with weights . The load definition gives the Dirichlet form of a Markov chain inequality; the common variance denominator then gives the spectral gap inequality. Identity transitions use empty paths.
Put . A transposition is routed using the word
of length . Each generator occurs at most twice; write its occurrence count as . For a fixed directed adjacent edge and each occurrence of in a word, there is exactly one starting permutation whose translated word crosses this edge at that occurrence. This follows because right multiplication by the prefix is a bijection of the symmetric group. Hence the uniform stationary factors cancel in the comparison load, leaving
The crude bounds , , and imply . Therefore
Only pairs with contribute, and there are of these. Keeping this restriction gives the stronger and . Neither comparison bound needs the exact adjacent-shuffle spectral gap; the normalization repair of the reference kernel is essential.
Choose two positions independently and uniformly from and swap them. This Markov chain on the symmetric group holds with probability and assigns probability to each unordered transposition. Its stationary distribution is uniform, and its ordinary spectral gap is for . The latter follows from the Aldous spectral gap theorem: the single-label continuous-time Markov chain has rate between every pair of positions, whose mean-zero eigenvalues are all .
For the random adjacent transposition shuffle with , the generator is the interchange process on a path graph with each edge rate . The Aldous spectral gap theorem reduces its spectral gap to that of the single-label random walk. The eigenfunctions , for , have generator eigenvalues ; direct substitution verifies both the interior and endpoint equations. Consequently .