We describe the first algorithms to compute minimum cuts in surface-embedded graphs in nearlinear time. Given an undirected graph embedded on an orientable surface of genus g, with two specified vertices s and t, our algorithm computes a minimum (s, t)-cut in g O(g) n log n time. Except for the special case of planar graphs, for which O(n log n)-time algorithms have been known for more than 20 years, the best previous time bounds for finding minimum cuts in embedded graphs follow from algorithms for general sparse graphs. A slight generalization of our minimum-cut algorithm computes a minimum-cost subgraph in every Z2-homology class. We also prove that finding a minimum-cost subgraph homologous to a single input cycle is NP-hard.

We describe the first algorithms to compute maximum flows in surface-embedded graphs in nearlinear time. Specifically, given an undirected graph embedded on an orientable surface of genus g, with two specified vertices s and t, we can compute a maximum (s, t)-flow in O(g 7 n log 2 n log 2 C) time for integer capacities that sum to C, or in (g log n) O(g) n time for real capacities. Except for the special case of planar graphs, for which an O(n log n)-time algorithm has been known for 20 years, the best previous time bounds for maximum flows in surface-embedded graphs follow from algorithms for general sparse graphs. Our key insight is to optimize the relative homology class of the flow, rather than directly optimizing the flow itself. A dual formulation of our algorithm computes the minimum-cost cycle or circulation in a given (real or integer) homology class.

We present a randomized algorithm for finding maximum matchings in planar graphs in time O(n ω/2), where ω is the exponent of the best known matrix multiplication algorithm. Since ω < 2.38, this algorithm breaks through the O(n 1.5) barrier for the matching problem. This is the first result of this kind for general planar graphs. We also present an algorithm for generating perfect matchings in planar graphs uniformly at random using O(n ω/2) arithmetic operations. Our algorithms are based on the Gaussian elimination approach to maximum matchings introduced in [1].

The eigenproblem for an n-by-n matrix A is the problem of the approximation (within a relative error bound 2 \Gammab ) of all the eigenvalues of the matrix A and computing the associated eigenspaces of all these eigenvalues. We show that the arithmetic complexity of this problem is bounded by O(n 3 + (n log 2 n) log b). If the characteristic and minimum polynomials of the matrix A coincide with each other (which is the case for generic matrices of all classes of general and special matrices that we consider), then the latter deterministic cost bound can be replaced by the randomized bound O(KA (2n) + n 2 + (n log 2 n) log b) where KA (2n) denotes the cost of the computation of the 2n \Gamma 1 vectors A i v, i = 1; : : : ; 2n \Gamma 1, maximized over all n-dimensional vectors v; KA (2n) = O(M(n) log n), for M(n) = o(n 2:376 ) denoting the arithmetic complexity of n \Theta n matrix multiplication. This bound on the complexity of the eigenproblem is optimal up to a logar...

This paper gives improved parallel methods for several exact factorizations of some classes of symmetric positive definite (SPD) matrices. Our factorizations also provide us similarly efficient algorithms for exact computation of the solution of the corresponding linear systems (which need not be SPD), and for finding rank and determinant magnitude. We assume the input matrices have entries that are rational numbers expressed as a ratio of integers with at most a polynomial number of bits #. We assume a PRAM model of parallel computation, with unit cost arithmetic operations, including division, over a finite field. We require only bit precision O(n(# + log n)), which is the asymptotically optimal bit precision for # log n. Our algorithms are randomized, giving the outputs with high likelihood . We compute LU and QR factorizations for dense matrices, and LU factorizations of sparse matrices which are s(n)- separable, reducing the known parallel time bounds for these factorizations from #(log n), without an increase in processors (matching the best known work bounds of known parallel algorithms with polylog time bounds). Using the same parallel algorithm specialized to structured matrices, we compute LU factorizations for Toeplitz matrices and matrices of bounded displacement rank in time O(log n) with n log log n processors, reducing by a nearly linear factor the best previous processor bounds for polylog times (however, these prior works did not generally require unit cost division over a finite field). We use this result to solve in the same bounds: polynomial resultant; and Pade approximants of rational functions; and in a factor O(log n) more time: polynomial greatest common divisors (GCD) and extended GCD; again reducing the...

This article, along with [Elkadi and Mourrain 1996], explain the correlation between residue theory and the Dixon matrix, which yields an alternative method for studying and approximating all common solutions. In 1916, Macaulay [1916] constructed a matrix whose determinant is a multiple of the classical resultant for n homogeneous polynomials in n variables. The Macaulay matrix si16 multaneously generalizes the Sylvester matrix and the coefficient matrix of a system of linear equations [Kapur and Lakshman Y. N. 1992]. As the Dixon formulation, the Macaulay determinant is a multiple of the resultant. Macaulay, however, proved that a certain minor of his matrix divides the matrix determinant so as to yield the exact resultant in the case of generic homogeneous polynomials. Canny [1990] has invented a general method that perturbs any polynomial system and extracts a non-trivial projection operator. Using recent results pertaining to sparse polynomial systems [Gelfand et al. 1994, Sturmfels 1991], a matrix formula for computing the sparse resultant of n + 1 polynomials in n variables was given by Canny and Emiris [1993] and consequently improved in [Canny and Pedersen 1993, Emiris and Canny 1995]. The determinant of the sparse resultant matrix, like the Macaulay and Dixon matrices, only yields a projection operation, not the exact resultant. Here, sparsity means that only certain monomials in each of the n + 1 polynomials have non-zero coefficients. Sparsity is measured in geometric terms, namely, by the Newton polytope

We present a linear work parallel iterative algorithm for solving linear systems involving Laplacians of planar graphs. In particular, if Ax = b, where A is the Laplacian of any planar graph with n nodes, the algorithm produces a vector ¯x such that ||x − ¯x||A ≤ ɛ, in O(n 1/6+c log(1/ɛ)) parallel time, doing O(n log(1/ɛ)) work, where c is any positive constant. One of the key ingredients of the solver, is an O(nk log 2 k) work, O(k log n) time, parallel algorithm for decomposing any embedded planar graph into components of size O(k) that are delimited by O(n / √ k) boundary edges. The result also applies to symmetric diagonally dominant matrices of planar structure.