Perron similarities and the nonnegative inverse eigenvalue problem

The longstanding <italic>nonnegative inverse eigenvalue problem</italic> (NIEP) is to determine which multisets of complex numbers occur as the spectrum of an entry-wise nonnegative matrix. Although there are some well-known necessary conditions, a solution to the NIEP is far from known. An invertible matrix is called a <italic>Perron similarity</italic> if it diagonalizes an irreducible, nonnegative matrix. Johnson and Paparella [Linear Algebra Appl. <bold>493</bold> (2016), pp. 281–300] developed the theory of real Perron similarities. Here, we fully develop the theory of complex Perron similarities. Each Perron similarity gives a nontrivial polyhedral cone and convex polytope of realizable spectra (thought of as vectors in complex Euclidean space). The extremals of these convex sets are finite in number, and their determination for each Perron similarity would solve the diagonalizable NIEP, a major portion of the entire problem. By considering Perron similarities of certain realizing matrices of Type I Karpelevič arcs, large portions of realizable spectra are generated for a given positive integer. This is demonstrated by producing a nearly complete geometrical representation of the spectra of four-by-four stochastic matrices. Similar to the Karpelevič region, it is shown that the subset of complex Euclidean space comprising the spectra of stochastic matrices is compact and star-shaped. <italic>Extremal</italic> elements of the set are defined and shown to be on the boundary. It is shown that the polyhedral cone and convex polytope of the <italic>discrete Fourier transform (DFT) matrix</italic> corresponds to the conical hull and convex hull of its rows, respectively. Similar results are established for multifold Kronecker products of DFT matrices and multifold Kronecker products of DFT matrices and Walsh matrices. These polytopes are of great significance with respect to the NIEP because they are <italic>extremal</italic> in the region comprising the spectra of stochastic matrices. Implications for further inquiry are also given.