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An introspective algorithm for the integer determinant  [PDF]
Jean-Guillaume Dumas,Anna Urbanska
Computer Science , 2005,
Abstract: We present an algorithm computing the determinant of an integer matrix A. The algorithm is introspective in the sense that it uses several distinct algorithms that run in a concurrent manner. During the course of the algorithm partial results coming from distinct methods can be combined. Then, depending on the current running time of each method, the algorithm can emphasize a particular variant. With the use of very fast modular routines for linear algebra, our implementation is an order of magnitude faster than other existing implementations. Moreover, we prove that the expected complexity of our algorithm is only O(n^3 log^{2.5}(n ||A||)) bit operations in the dense case and O(Omega n^{1.5} log^2(n ||A||) + n^{2.5}log^3(n||A||)) in the sparse case, where ||A|| is the largest entry in absolute value of the matrix and Omega is the cost of matrix-vector multiplication in the case of a sparse matrix.
Improved Algorithm for Bosonized Fermion Determinant  [PDF]
A. A. Slavnov
Physics , 1996,
Abstract: Following the line of \cite{AS} we propose an improved algorithm which allows to calculate a D-dimensional fermion determinant integrating the exponent of D+1 dimensional Hermitean bosonic action. For a finite extra dimension the corrections decrease exponentially.
A New Algorithm for the Determinant and the Inverse of Banded Matrices
Mohamed Elouafi,Driss Aiat Hadj Ahmed
Open Access Library Journal (OALib Journal) , 2014, DOI: 10.4236/oalib.1100543
Abstract: In the current article, the authors present a new recurrence formula for the determinant of a banded matrix. An algorithm for inverting general banded matrices is derived.
The RHMC algorithm for theories with unknown spectral bounds  [PDF]
J. B. Kogut,D. K. Sinclair
Physics , 2006, DOI: 10.1103/PhysRevD.74.114505
Abstract: The Rational Hybrid Monte Carlo (RHMC) algorithm extends the Hybrid Monte Carlo algorithm for lattice QCD simulations to situations involving fractional powers of the determinant of the quadratic Dirac operator. This avoids the updating increment ($dt$) dependence of observables which plagues the Hybrid Molecular-dynamics (HMD) method. The RHMC algorithm uses rational approximations to fractional powers of the quadratic Dirac operator. Such approximations are only available when positive upper and lower bounds to the operator's spectrum are known. We apply the RHMC algorithm to simulations of 2 theories for which a positive lower spectral bound is unknown: lattice QCD with staggered quarks at finite isospin chemical potential and lattice QCD with massless staggered quarks and chiral 4-fermion interactions ($\chi$QCD). A choice of lower bound is made in each case, and the properties of the RHMC simulations these define are studied. Justification of our choices of lower bounds is made by comparing measurements with those from HMD simulations, and by comparing different choices of lower bounds.
A Randomized Algorithm for Approximating the Log Determinant of a Symmetric Positive Definite Matrix  [PDF]
Christos Boutsidis,Petros Drineas,Prabhanjan Kambadur,Anastasios Zouzias
Computer Science , 2015,
Abstract: We introduce a novel algorithm for approximating the logarithm of the determinant of a symmetric positive definite matrix. The algorithm is randomized and proceeds in two steps: first, it finds an approximation to the largest eigenvalue of the matrix after running a few iterations of the so-called "power method" from the numerical linear algebra literature. Then, using this information, it approximates the traces of a small number of matrix powers of a specially constructed matrix, using the method of Avron and Toledo~\cite{AT11}. From a theoretical perspective, we present strong worst-case analysis bounds for our algorithm. From an empirical perspective, we demonstrate that a C++ implementation of our algorithm can approximate the logarithm of the determinant of large matrices very accurately in a matter of seconds.
Bounds for the Second Hankel Determinant of Certain Univalent Functions  [PDF]
Lee See Keong,V. Ravichandran,Shamani Supramaniam
Mathematics , 2013,
Abstract: The estimates for the second Hankel determinant a_2a_4-a_3^2 of analytic function f(z)=z+a_2 z^2+a_3 z^3+...b for which either zf'(z)/f(z) or 1+zf"(z)/f'(z) is subordinate to certain analytic function are investigated. The estimates for the Hankel determinant for two other classes are also obtained. In particular, the estimates for the Hankel determinant of strongly starlike, parabolic starlike, lemniscate starlike functions are obtained.
Risk bounds for statistical learning  [PDF]
Pascal Massart,élodie Nédélec
Mathematics , 2007, DOI: 10.1214/009053606000000786
Abstract: We propose a general theorem providing upper bounds for the risk of an empirical risk minimizer (ERM).We essentially focus on the binary classification framework. We extend Tsybakov's analysis of the risk of an ERM under margin type conditions by using concentration inequalities for conveniently weighted empirical processes. This allows us to deal with ways of measuring the ``size'' of a class of classifiers other than entropy with bracketing as in Tsybakov's work. In particular, we derive new risk bounds for the ERM when the classification rules belong to some VC-class under margin conditions and discuss the optimality of these bounds in a minimax sense.
Compression algorithm for multi-determinant wave functions  [PDF]
Gihan L. Weerasinghe,Pablo Lopez Rios,Richard J. Needs
Physics , 2013, DOI: 10.1103/PhysRevE.89.023304
Abstract: A compression algorithm is introduced for multi-determinant wave functions which can greatly reduce the number of determinants that need to be evaluated in quantum Monte Carlo calculations. We have devised an algorithm with three levels of compression, the least costly of which yields excellent results in polynomial time. We demonstrate the usefulness of the compression algorithm for evaluating multi-determinant wave functions in quantum Monte Carlo calculations, whose computational cost is reduced by factors of between 1.885(3) and 25.23(4) for the examples studied. We have found evidence of sub-linear scaling of quantum Monte Carlo calculations with the number of determinants when the compression algorithm is used.
New lower bounds for the maximal determinant problem  [PDF]
William P. Orrick,Bruce Solomon,Roland Dowdeswell,Warren D. Smith
Mathematics , 2003,
Abstract: We report new world records for the maximal determinant of an n-by-n matrix with entries +/-1. Using various techniques, we beat existing records for n=22, 23, 27, 29, 31, 33, 34, 35, 39, 45, 47, 53, 63, 69, 73, 77, 79, 93, and 95, and we present the record-breaking matrices here. We conjecture that our n=22 value attains the globally maximizing determinant in its dimension. We also tabulate new records for n=67, 75, 83, 87, 91 and 99, dimensions for which no previous claims have been made. The relevant matrices in all these dimensions, along with other pertinent information, are posted at http://www.indiana.edu/~maxdet \.
Unquenched Studies Using the Truncated Determinant Algorithm  [PDF]
A. Duncan,E. Eichten,H. Thacker
Physics , 2001, DOI: 10.1016/S0920-5632(01)01781-9
Abstract: A truncated determinant algorithm is used to study the physical effects of the quark eigenmodes associated with eigenvalues below 420 MeV. This initial high statistics study focuses on coarse ($6^4$) lattices (with O($a^2$) improved gauge action), light internal quark masses and large physical volumes. Three features of full QCD are examined: topological charge distributions, string breaking as observed in the static energy and the eta prime mass.
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