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Home›Decision-making›Vector (L2) Normalization
MCDMNormalizationcrisp

Vector (L2) Normalization

VECTOR-NORMALIZATION (Vector (L2) Normalization) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L. Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

Outputs normalised matrix. No ranking produced; downstream method handles ranking.

Strengths & limitations

Strengths
  • Follows a transparent, reproducible computational procedure that can be audited step by step.
  • Handles multiple criteria of differing scales and units within a single decision matrix.
Limitations
  • Results depend on the chosen normalisation, weights, and parameter settings.

Sources

  1. Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, Berlin DOI: 10.1007/978-3-642-48318-9 ↗

How to cite this page

ScholarGate. (2026, June 2). Vector (L2) Normalization. ScholarGate. https://scholargate.app/en/decision-making/vector-normalization

Similar methods

NORM-VECTORMIN-MAX-NORMALIZATIONDIST-EUCLIDEANTOPSISLOGARITHMIC-NORMALIZATIONLINEAR-SUM-NORMALIZATIONLINEAR-MAX-NORMALIZATIONZ-SCORE-NORMALIZATION

Related reference concepts

Principal Component AnalysisDecision Support SystemsDecision MakingWeighted ScoresMultidimensional ScalingSupport Vector Classification

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — VECTOR-NORMALIZATION (Vector (L2) Normalization). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/vector-normalization · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Hwang, C. L. Yoon, K.
Subfamily
Normalization
Year
1981
Type
Euclidean norm-based column normalization
Value Space
crisp
Uncertainty
None
Compensation
N/A
Rank Reversal
No
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