Linear Sum Normalization: column-sum division (probability / stochastic normalisation)

Overview

Normalization (linear-sum, stochastic). Output typically normalized_matrix (higher value = preferred).

Strengths

• •Method-specific: Normalization (linear-sum, stochastic)

Edge cases

• •See F.steps and D.parameters for LINEAR-SUM-NORMALIZATION-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Bkz. LINEAR-SUM-NORMALIZATION F.steps citation_anchor'lar ve P.verification_status.

Worked example

1. 1.Adım 1 (F1): Benefit criterion j: r_ij = x_ij / Σ_k x_kj. Cost criterion j: first invert (y_ij = 1/x_ij), then r_ij = y_ij / Σ_k y_kj. Each column sums to 1. Formül: r_{ij} = \begin{cases} x_{ij}/\sum_{k}x_{kj} & j\in J^{+} \\ (1/x_{ij})/\sum_{k}(1/x_{kj}) & j\in J^{-} \end{cases} Anchor: Zavadskas et al. 1994 (column-sum normalisation; pending PDF page verification)

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