Read equality of the two positive series as equality of a finite total , so the sequences are summable. This is the majorization of summable sequences setting. If equality is instead allowed to mean , the assertion is false: the strictly decreasing sequences and satisfy every partial-sum inequality and have divergent equal extended totals. But a normalized nonnegative row of any doubly stochastic matrix averages numbers all at least , and cannot give . We prove the intended finite-total assertion constructively.
Since and , there is a finite such that . Put
The two-coordinate stochastic averaging matrix sends to . Move the first of these values to the front by a finite permutation, leaving the other values in decreasing order. The tail is
which is decreasing and strictly positive because .
We need the residual majorization in order to repeat the construction. For , the first values of are at least , while every target-tail value is at most . Thus
For , the residual initial sum is the old initial sum with removed:
The tail totals also agree, both equalling . Therefore the same step applies to and .
Iterating, let be the product of the first finite permutations and embedded averaging matrices. Each is a doubly stochastic matrix, each row is a finitely supported sequence, and the first entries of are . Later operations act only on tail rows, so row becomes permanently fixed once . Define to be that stabilized row. Then
For every fixed column and every , its first stabilized entries sum to at most one, since they are entries of column in . Consequently . By Tonelli's theorem,
All are strictly positive and every summand in the last expression is nonnegative, so for every . Thus both the row sums and the column sums are one, and
The finite-total argument is what prevents loss of column mass in the infinite construction. This completes the doubly stochastic realization of summable-sequence majorization.