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Column-overlap formula for the zero-sum ℓ1 coefficient
Statement
For a finite column-stochastic matrix , define when , and set when (then ). If denotes column , then
Thus is a computable induced contraction factor on the zero-sum subspace ; for every , .
Facts & Assumptions
Given: A finite column-stochastic matrix and the zero-sum subspace from Finite column-stochastic matrices and probability vectors.
Finite column-stochastic matrices and probability vectors: every column is nonnegative with coordinate sum one, , and .
Column-stochastic matrices preserve mass and contract ℓ1: P preserves coordinate sums and satisfies for every real vector z.
Proof
Given: The finite matrix and its columns .
Proof technique: decompose a zero-sum vector into positive and negative probability masses.
Let with . Write coordinatewise, where and . Since , the positive and negative masses agree, and their sum is one; hence . Therefore and are probability vectors and .
We have and . Thus . The coefficients are nonnegative and sum to one, so the triangle inequality gives . Taking the supremum over such proves the upper bound.
If and the maximum is positive, a maximizing pair has ; if the maximum is zero, any distinct pair maximizes it. Choose such a distinct pair and set . Then , , and , so the supremum is at least . If , both sides are zero by definition. This proves the first equality.
For two nonnegative probability columns , . Summing gives . Apply this to every pair of columns; taking the maximum of the left side is one minus the minimum overlap on the right.
If , [F2] bounds every norm in the defining supremum by one, so ; if , by definition. For nonzero , apply the definition of to and use homogeneity; for the inequality is immediate. Thus on all of .
Depends on
Used by
- Correct and certify a finite-chain stationary candidate Algorithm
- A small residual can hide a large stationary error Example
- Periodic iteration can fail while the stationary solve is well-defined Example
- Two absorbing classes retain initial-law-dependent weights Example
- m-step Dobrushin certificate for a stationary probability Theorem
Dependency tree · two levels
3 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Ilse C. F. Ipsen and Teresa M. Selee, Ergodicity Coefficients Defined by Vector Norms (standard reference, not scraped)