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Exact(4)

Where, A^ is the (I A) matrix with 1st column as all zero and a1,1 is equal to −1. x^ is the output vector with 1st row as endogenous f1.

The objective function depends on the matrix X of N intensity column vectors x i, a matrix W of K prototype column vectors w j and an N × K matrix A of binary assignment variables a ij ∈ {0, 1}.

We are given the following types of data: (i) a matrix A whose entries A i, j encode how often feature j occurs in sequence/structure pair i, and (ii) a vector y containing measured melting temperatures y i for experiment i. Constraints are now generated as follows.

where small value of a is to ensure the matrix P H P + a I a matrix with full rank.

Similar(55)

The k×3 matrix E i is a matrix of Gaussian random errors with mean 0, being a random variate from a matrix Gaussian distribution i.e. E i ~ N k,3 0, Σ, I3).

where J t,l (i) is a matrix of size N×N.

I am a matrix of lean meat.. Please be gentle lest you tear me apart.

where I is a matrix of size (K−N ×N, which is a block matrix of IDFT matrix.

Ions transfer between the pathways of I via a matrix of II when the polymers are intimately blended.

PCA decomposes the variation of matrix X into scores T, loadings P, and a residuals matrix E. P is an I × A matrix containing the A selected loadings and T is a J × A matrix containing the accompanying scores.

To do so, I calculated a matrix of distances (in meters) between any two households.

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