# Show matrix values matlab torrent

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When you multiply an array by a scalar, the scalar implicitly expands to be the same size as the other input. This is often referred to as scalar expansion. Generally accepted mathematical notation uses the capital letter I to denote identity matrices, matrices of various sizes with ones on the main diagonal and zeros elsewhere.

The original version of MATLAB could not use I for this purpose because it did not distinguish between uppercase and lowercase letters and i already served as a subscript and as the complex unit. So an English language pun was introduced. The function. This solution is called the inverse of A and is denoted A The determinant calculated by det is a measure of the scaling factor of the linear transformation described by the matrix.

When the determinant is exactly zero, the matrix is singular and no inverse exists. Some matrices are nearly singular , and despite the fact that an inverse matrix exists, the calculation is susceptible to numerical errors. The cond function computes the condition number for inversion , which gives an indication of the accuracy of the results from matrix inversion.

The condition number ranges from 1 for a numerically stable matrix to Inf for a singular matrix. It is seldom necessary to form the explicit inverse of a matrix. See mldivide for more information. The Kronecker product, kron X,Y , of two matrices is the larger matrix formed from all possible products of the elements of X with those of Y. If X is m -by- n and Y is p -by- q , then kron X,Y is mp -by- nq.

The elements are arranged such that each element of X is multiplied by the entire matrix Y :. The Kronecker product is often used with matrices of zeros and ones to build up repeated copies of small matrices. For example, if X is the 2-by-2 matrix. Aside from kron , some other functions that are useful to replicate arrays are repmat , repelem , and blkdiag.

In cases where you want to calculate the norm of each row or column of a matrix, you can use vecnorm :. MATLAB supports multithreaded computation for a number of linear algebra and element-wise numerical functions. These functions automatically execute on multiple threads. For a function or expression to execute faster on multiple CPUs, a number of conditions must be true:.

The function performs operations that easily partition into sections that execute concurrently. These sections must be able to execute with little communication between processes. They should require few sequential operations. The data size is large enough so that any advantages of concurrent execution outweigh the time required to partition the data and manage separate execution threads.

For example, most functions speed up only when the array contains several thousand elements or more. The operation is not memory-bound; processing time is not dominated by memory access time. As a general rule, complicated functions speed up more than simple functions. The matrix analysis functions det , rcond , hess , and expm also show significant increase in speed on large double-precision arrays. Choose a web site to get translated content where available and see local events and offers.

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