Eigenvalue Of Irreducible Nonnegative Matrix Optimization Algorithmioned

With the development of computer and communication technology, matrix theory hasbecome more common. Non-negative matrix is an important matrix theory in the matrixclass, non-negative matrix theory is to study non-negative matrix whose matrix elementsare non-negative. If matrix A is irreducible non-negative matrix, then the vector afternormalized is eigenvector corresponding to perron root of A.Non-negative matrix theory of spectral analysis in various matrices has a wide rangeof applications. The perron eigenvalue calculation problems of Non-negative matrix inmathematical analysis and engineering applications are important.In this article, based on the full investigation and concluded on the basis of theprevious basic algorithm, we propose our own algorithms. The basic idea is to combineformula and inverse collatziteration. In this paper we first discuss the conditions of thealgorithm, and then test matrix is used to verify the accuracy and convergence of thealgorithm, the calculation results is compared with the previous algorithm which has betteraccuracy.We take a closer look at the computational time of all algorithms including ouralgorithm on irreducible matrices. From all experiments, we found that our algorithmproduced the best results. Also, with increasingly use of nonnegative matrices in variousapplications, our algorithm will be one of the best choices among others.