what is identity matrix?
554067
The identity matrix is a matrix that when multiplied to another matrix results in that same matrix. It is similar to the number 1 because 1 multiplied by any number gives the same number.\n\nThe identity matrix can be identified by the presence of 1s diagonally across it like a back slash. The rest of the numbers are 0.\n\ne.g.\n10\n01\n\n100\n010\n001\n\n...
618738
I can't give you a definiton because I really didn't understand anything I read about matrices!!!  But here are a few links that may be helpful to you.
159293
Depends on what you are analyzing.  Matrix algebra is used extensively in communications and signal processing applications.  Not very much, though, in financial applications.
199213
You probably mean eigen-values. We say that x is an eigen value for the square matrix A if there is a vector v such that Av=xv. Eigen-values are usually found by solving det(A-xI)=0\nwhere I is the identity matrix.
813865
The singular value decomposition for a matrix A writes A as a product (hanger)(stretcher)(aligner). \n \n\nIt's an amazing and useful fact that every m x n matrix has a singular value decomposition. \n\n-------------------\nIn linear algebra, the singular value decomposition (SVD) is an important factorization of a rectangular real or complex matrix, with several applications in signal processing and statistics.\n\nThe SVD can be seen as a generalization of the spectral theorem to arbitrary, not necessarily square, matrices. The spectral theorem says that normal matrices can be unitarily diagonalized using a basis of eigenvectors.\n\n---------------------------------\nSingular value decomposition takes a rectangular matrix of gene expression data (defined as A, where A is a n x p matrix) in which the n rows represents the genes, and the p columns represents the experimental conditions.\n\n---------------------------------\nA very powerful set of techniques dealing with sets of equations or matrices that are either singular or numerically very close to singular is the so-called singular value decomposition (SVD). SVD allows one to diagnose the problems in a given matrix and provides numerical answer as well.\n\nAny m x n matrix a (m >= n) can be written as the product of an m x n column-orthogonal matrix u, an n x n diagonal matrix with positive or zero elements, and the transpose of an n x n orthogonal matrix v:\n\n\nI hope that all these are useful, good luck!!!!!!!!!!!!
1113512
your registered name , which is your identity
440750
Density Matrix.\n\ndiagonal part + nondiagonal part or\nstate probabilities + transition probabilities.
1087743
Matrix - the no. of rows and columns are not necessarily equal.\nDeterminants - they r equal.\nMatrix - elements are enclosed in (), { }, or [ ]\nDeterminants - elements r shown between two vertical lines. I I\nMatrix - it cant hav a value. its just an arrangement.\nDeterminant - has a value.\nMatrix - if all elements r multiplied by a constant the matrix is multiplied to the constant.\nDeterminant - if each of any row or column is multiplied by a constant, the determinant is mulitplied to the constant.\nPhew * Need some lemonade...
115853
#include <iostream>\n#include <vector>\nusing namespace std;\n#define rep(i, n) for((i) = 0; (i) < (n); (i)++)\n#define rep2(i, k, n) for((i) = k; (i) < (n); (i)++)\nvoid f21();\nint n;\ndouble mat[100][100];\nint main()\n{\nwhile(true)\n{\ncin >> n;\nif(!n) break;\nmemset(mat, 0, sizeof(mat));\nf21();\n}\nreturn 0;\n}\n\nvoid f21()\n{\ncout << "****************************************" << endl;\ncout << "*          INVERSE OF MATRIX           *" << endl;\ncout << "****************************************" << endl;\n\ncout << "ORDER OF THE MATRIX: " << n << endl;\ncout << "ENTER ELEMENT ROW-WISE" << endl;\nint m = 2*n;\nint i, j, k;\nrep(i, n) {\nrep(j, n) {\ncin >> mat[i][j];\ncout <<"&#92;t";\nprintf("%5.3lf", mat[i][j]);\n}\nrep2(j, n, m) {\nif(i == (j-n)){\nmat[i][j] = 1;\ncontinue;\n}\nmat[i][j] = 0;\n}\ncout << endl;\n}\n\ncout << endl << "INVERSE MATRIX: " << endl;\ndouble pivot, comm;\nrep(i, n) {\npivot = mat[i][i];\nif(mat[i][i] == 0){\ncout << "DIVISION BY ZERO." <<endl;\nreturn;\n}\nrep(j, m) mat[i][j] = mat[i][j]/pivot;\nrep(j, n) {\nif(i == j) continue;\ncomm = mat[j][i];\nrep2(k, i, m) {\nmat[j][k] = mat[j][k] - (comm*mat[i][k]);\n}\n}\n}\n\nrep(i, n) {\nrep2(j, n, m) {\ncout <<"&#92;t";\nprintf("%6.4lf", mat[i][j]);\n}\ncout << endl;\n}\ncout << endl;\n}\n\n/*This is writen in C++ this will output the inverse of a Matrix\ninput:\n4\n1 2 3 4\n2 1 4 2\n2 5 7 12\n1 -1 3 11\n\n5\n1.173 -0.634 0 0 0\n-0.539 1.173 -0.634 0 0\n0 -0.539 1.173 -0.634 0\n0 0 -0.539 1.173 -0.634\n0 0 0 -0.539 1.173\n\n0\n*/
37157
A covariance matrix is a useful tool in statistics. \n\nA matrix is an array of numbers, in the form of a rectangle, with several rows and several columns. A covariance matrix is a special kind of matrix that is used to find connections between variables. \n\nFor example, if you have N random variables, and you want to know how they are related to each other, then you can compute a covariance matrix. The covariance matrix for N random variables has N rows and N columns, that is, it is an N x N matrix. Note that the covariance matrix is a square matrix (it has the same number of rows and columns; in this case, N).\n \n\nThe diagonal elements of the covariance matrix give the variance of each of the random variables in turn; that is, they describe how much each of these random variables varies. \n\nThe off-diagonal elements describe how the random variable Xj corresponding to the jth row and the random variable Xk corresponding to the kth column behave. For example, if changes in Xj do not have any effect on Xk, then the covariance matrix value at row j and column k, C(j,k) will be zero: C(j,k)=0. If when Xj increases, Xk increases, then C(j,k)>0. If increasing Xj causes Xk to decrease, then C(j,k)<0.\n\nOf course, the details about how to compute the covariance matrix C(j,k) are slightly more complicated than this, but this gives you a sort of idea about what the covariance matrix means.
941211
In a square matrix, the determinant is the difference of the diagonals. Ex: a   b\n                        c   d\n The determinant is ad - bc.
909215
It can be encoded with another 1*2 matrix through addition.\n\nFor example,\n\noriginal matrix = (x   y)\n\nencoding matrix = (a b)\n\nCoded matrix = (x+a    y+b)
95774
The RANK of matrix A, an m x n matrix, is the dimension of the row spaces or the column space. Or, the number of basic variables in Ax = 0. \n\nUsually written as rank A.\n\nAlso, for m x n matrix A, the row space and the column space have the same dimension, \n\nwritten dim R(A) = dim C(A).
4598
Recall that the eigenvalues are the values of x such that det(xI-AB)=0.  Also det is a multiplicative function.  So eigenvalues are the values of x such that det(xA^(-1)-B)=0 (left multiply by A^(-1)).  But these are the same values of x such that det(xI-BA)=0 (right multiply by A.
201316
This website may help you.  Good luck.\n\nhttp://www.sosmath.com/matrix/matrix.html
1185845
Gray-level Co-occurrence Matrix\nGray-level co-occurrence matrix is the two dimensional matrix of joint probabilities Pd,r(i,j) between pairs of pixels, separated by a distance, d, in a given direction, r. It is popular in texture description and based on the repeated occurrence of some gray level configuration in the texture; this configuration varies rapidly with distance in fine textures, slowly in coarse textures (Haralick et al., 1973).\n\nFinding texture features from gray-level co-occurrence matrix for texture classification in this experiment are based on these criteria:\n\n\nGO TO THE FOLLOWING LINK
235332
It's the Identity Law: if a=b, then b=a.