The Analytic Hierarchy Process: Planning, Priority Setting, Resource AllocationMcGraw-Hill International Book Company, 1980 - 287 páginas |
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Página 66
... eigenvector . Another and perhaps more relevant way to revise judgments relates to selecting the largest of the ... left eigenvectors ( these satisfy vA = 2v ) and right eigenvectors ( these satisfy the familiar form Aw = 2w ) . For λ = max a ...
... eigenvector . Another and perhaps more relevant way to revise judgments relates to selecting the largest of the ... left eigenvectors ( these satisfy vA = 2v ) and right eigenvectors ( these satisfy the familiar form Aw = 2w ) . For λ = max a ...
Página 97
... eigenvector has the form { a ( t ) / [ a ( t ) +1 ] , 1 / [ a ( t ) +1 ] } . The normalized left eigenvector is the componentwise reciprocal of this given by { 1 / a ( t ) [ a ( t ) +1 ] , 1 / [ a ( t ) +1 ] } . Cubic Case In a ...
... eigenvector has the form { a ( t ) / [ a ( t ) +1 ] , 1 / [ a ( t ) +1 ] } . The normalized left eigenvector is the componentwise reciprocal of this given by { 1 / a ( t ) [ a ( t ) +1 ] , 1 / [ a ( t ) +1 ] } . Cubic Case In a ...
Página 191
... left eigenvector components of a reciprocal positive 3 by 3 matrix are the reciprocals of the normalized right eigenvector components . The proof requires use of the following equality in the expressions for w and v given in Chap . 5 ...
... left eigenvector components of a reciprocal positive 3 by 3 matrix are the reciprocals of the normalized right eigenvector components . The proof requires use of the following equality in the expressions for w and v given in Chap . 5 ...
Contenido
PART ONE THE ANALYTIC HIERARCHY PROCESS | 1 |
A formal approach | 4 |
Instructive examples | 37 |
Derechos de autor | |
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The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation Thomas L. Saaty Vista de fragmentos - 1990 |
Términos y frases comunes
a₁ activities allocation alternatives Amax analysis Analytic Hierarchy Process applications approach b₁ binary matrix C₁ clusters coefficients column vector compared complete graph components composite consistency corresponding cost criteria D₁ decision defined denote derived determine diagonal directed graph dominance edges eigenvalue problem elements entries equal equation estimate example factors given gives goal graph hence hierarchy level impact importance influence irreducible judgments least squares left eigenvector linear mathematical measurement method Multidimensional Scaling multiple nonnegative normalized Note objectives obtain outcome overall pairwise comparison pairwise comparison matrix perturbation positive primitive matrix priority vector PROOF rank ratio scale reciprocal matrix relations relative respect result right eigenvector root mean square row sums scenario solution stochastic matrix structure Sudan supermatrix Table Theorem theory unity utility function v₁ variables vertex vertices w₁ weights zero