scholarly journals Generalizing and derandomizing Gurvits's approximation algorithm for the permanent

2014 ◽  
Vol 14 (7&8) ◽  
pp. 541-559
Author(s):  
Scott Aaronson ◽  
Travis Hance
Keyword(s):  
Quantum Optics ◽  
Randomized Algorithm ◽  
Decision Problems ◽  
Complex Numbers ◽  
Nonnegative Matrices ◽  
Outstanding Problem ◽  
The Matrix ◽  
Bounded Norm ◽  
Sampling Problems ◽  
Special Case

Around 2002, Leonid Gurvits gave a striking randomized algorithm to approximate the permanent of an $n\times n$ matrix A. The algorithm runs in $O( n^{2}/\varepsilon^{2})$ time, and approximates $\operatorname*{Per}( A)$ to within $\pm\varepsilon\left\Vert A\right\Vert ^{n}$ additive error. A major advantage of Gurvits's algorithm is that it works for arbitrary matrices, not just for nonnegative matrices. \ This makes it highly relevant to quantum optics, where the permanents of bounded-norm complex matrices play a central role. (In particular, $n\times n$ permanents arise as the transition amplitudes for $n$ identical photons.) Indeed, the existence of Gurvits's algorithm is why, in their recent work on the \textit{hardness} of quantum optics, Aaronson and Arkhipov (AA) had to talk about sampling problems rather than ordinary decision problems. In this paper, we improve Gurvits's algorithm in two ways. First, using an idea from quantum optics, we generalize the algorithm so that it yields a better approximation when the matrix A has either repeated rows or repeated columns. Translating back to quantum optics, this lets us classically estimate the probability of any outcome of an AA-type experiment -- even an outcome involving multiple photons "bunched" in the same mode -- at least as well as that probability can be estimated by the experiment itself. It also yields a general upper bound on the probabilities of "bunched" outcomes, which resolves a conjecture of Gurvits and might be of independent physical interest. Second, we use $\varepsilon$-biased sets to derandomize Gurvits's algorithm, in the special case where the matrix A is nonnegative. More interestingly, we generalize the notion of $\varepsilon$-biased sets to the complex numbers, construct "complex $\varepsilon$-biased sets", then use those sets to derandomize even our generalization of Gurvits's algorithm to the case (again for nonnegative A) where some rows or columns are identical. Whether Gurvits's algorithm can be derandomized for general A remains an outstanding problem.

Determination of the phase structure of polymerblends by electron microscopy

1978 ◽  
Vol 36 (1) ◽  
pp. 492-493
Author(s):  
Dr. G. Kaemof
Keyword(s):  
Screw Extruder ◽  
Electron Microscopic ◽  
Thin Sections ◽  
Single Screw Extruder ◽  
Transmission Electron ◽  
The Matrix ◽  
Twin Screw ◽  
Special Case ◽  
Microscopic Investigations

A mixture of polycarbonate (PC) and styrene-acrylonitrile-copolymer (SAN) represents a very good example for the efficiency of electron microscopic investigations concerning the determination of optimum production procedures for high grade product properties.The following parameters have been varied:components of charge (PC : SAN 50 : 50, 60 : 40, 70 : 30), kind of compounding machine (single screw extruder, twin screw extruder, discontinuous kneader), mass-temperature (lowest and highest possible temperature).The transmission electron microscopic investigations (TEM) were carried out on ultra thin sections, the PC-phase of which was selectively etched by triethylamine.The phase transition (matrix to disperse phase) does not occur - as might be expected - at a PC to SAN ratio of 50 : 50, but at a ratio of 65 : 35. Our results show that the matrix is preferably formed by the components with the lower melting viscosity (in this special case SAN), even at concentrations of less than 50 %.

scholarly journals The Algebraic Degree of Phase-Type Distributions

2010 ◽  
Vol 47 (03) ◽  
pp. 611-629
Author(s):  
Mark Fackrell ◽  
Qi-Ming He ◽  
Peter Taylor ◽  
Hanqin Zhang
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Polynomial Algorithm ◽  
Algebraic Degree ◽  
Nonnegative Matrices ◽  
Stieltjes Transform ◽  
Special Cases ◽  
Phase Type ◽  
Phase Type Distributions ◽  
The Matrix

This paper is concerned with properties of the algebraic degree of the Laplace-Stieltjes transform of phase-type (PH) distributions. The main problem of interest is: given a PH generator, how do we find the maximum and the minimum algebraic degrees of all irreducible PH representations with that PH generator? Based on the matrix exponential (ME) order of ME distributions and the spectral polynomial algorithm, a method for computing the algebraic degree of a PH distribution is developed. The maximum algebraic degree is identified explicitly. Using Perron-Frobenius theory of nonnegative matrices, a lower bound and an upper bound on the minimum algebraic degree are found, subject to some conditions. Explicit results are obtained for special cases.

Krom formulas with one dyadic predicate letter

1976 ◽  
Vol 41 (2) ◽  
pp. 341-362 ◽  
Author(s):  
Harry R. Lewis
Keyword(s):  
Normal Forms ◽  
Decision Problems ◽  
The Matrix ◽  
Reduction Class ◽  
Complex Construction ◽  
Full Class

Let Kr be the class of all those quantificational formulas whose matrices are conjunctions of binary disjunctions of signed atomic formulas. Decision problems for subclasses of Kr do not invariably coincide with those for the corresponding classes of quantificational formulas with unrestricted matrices; for example, the ∀∃∀ prefix subclass of Kr is solvable, but the full ∀∃∀ class is not ([AaLe],- [KMW]). Moreover, the straightforward encodings of automata which have been used to show the unsolvability of various subclasses of Kr ([Aa], [Bö], [AaLe]) yield but little information about signature subclasses, i.e. subclasses determined by the number and degrees of the predicate letters occurring in a formula. By a new and highly complex construction Theorem 1 establishes the best possible result on classification by signature.Theorem 1. Let C be the class of all formulas in Kr with a single predicate letter, which is dyadic; then C is a reduction class for satisfiability.Thus a signature subclass of Kr is solvable just in case the corresponding class of unrestricted quantificational formulas is solvable, to wit, just in case no predicate letter of degree exceeding one may occur. To obtain a richer classification by signature we consider further restrictions on the matrix. Let Or be the class of formulas in Kr having disjunctive normal forms with only two disjuncts. Theorem 2 sharpens Orevkov's proof of the unsolvability of Or ([Or]; see also [LeGo]).Theorem 2. Let D be the class of formulas in Or with just two predicate letters, both pentadic; then D is a reduction class for satisfiability.

scholarly journals The Categorization and Structural Prediction of Transition Metal Carbonyl Clusters Using 14n Series Numerical Matrix

2016 ◽  
Vol 8 (1) ◽  
pp. 109
Author(s):  
Enos Masheija Rwantale Kiremire
Keyword(s):  
Transition Metal ◽  
Valence Electron ◽  
Metal Carbonyl ◽  
Electron Content ◽  
Constant Number ◽  
Carbonyl Clusters ◽  
Structural Prediction ◽  
Metal Carbonyl Clusters ◽  
The Matrix ◽  
Special Case

<p>A matrix table of valence electron content of carbonyl clusters has been created using the 14n-based series. The numbers so generated form an array of series which conform precisely with valence electron contents of carbonyl clusters. The renowned 18 electron rule is a special case of 14n+4 series. Similarly, the 16 electron rule is another special case of the 14n+2 series. Categorization of the carbonyl clusters using the matrix table of series has been demonstrated. The table is so organized that clusters numerically represented can easily be compared and analyzed. The numbers that are diagonally arranged from right to left represent capping series. The row from right to left represents a decrease in valence electron content with increase in cluster linkages. The variation of cluster shapes of constant number of skeletal elements especially four or more may be monitored or compared with the variation with the valence electron content.</p>

scholarly journals Computing mu-values for Representations of Symmetric Groups in Engineering Systems

2018 ◽  
Vol 11 (3) ◽  
pp. 774-792
Author(s):  
Mutti-Ur Rehman ◽  
M. Fazeel Anwar
Keyword(s):  
Control Systems ◽  
Symmetric Groups ◽  
Singular Values ◽  
Linear Control ◽  
Linear Control Systems ◽  
Complex Numbers ◽  
Engineering Systems ◽  
Matrix Representations ◽  
The Matrix

In this article we consider the matrix representations of finite symmetric groups Sn over the filed of complex numbers. These groups and their representations also appear as symmetries of certain linear control systems [5]. We compute the structure singular values (SSV) of the matrices arising from these representations. The obtained results of SSV are compared with well-known MATLAB routine mussv.

scholarly journals Results on a Super Strong Exponential Time Hypothesis

2020 ◽  
Vol 34 (09) ◽  
pp. 13700-13703
Author(s):  
Nikhil Vyas ◽  
Ryan Williams
Keyword(s):  
Randomized Algorithm ◽  
Time Algorithm ◽  
Critical Threshold ◽  
Polynomial Method ◽  
Variable Ratio ◽  
Sat Solving ◽  
Worst Case ◽  
Exponential Time ◽  
Exponential Time Hypothesis ◽  
Special Case

All known SAT-solving paradigms (backtracking, local search, and the polynomial method) only yield a 2n(1−1/O(k)) time algorithm for solving k-SAT in the worst case, where the big-O constant is independent of k. For this reason, it has been hypothesized that k-SAT cannot be solved in worst-case 2n(1−f(k)/k) time, for any unbounded ƒ : ℕ → ℕ. This hypothesis has been called the “Super-Strong Exponential Time Hypothesis” (Super Strong ETH), modeled after the ETH and the Strong ETH. We prove two results concerning the Super-Strong ETH:1. It has also been hypothesized that k-SAT is hard to solve for randomly chosen instances near the “critical threshold”, where the clause-to-variable ratio is 2k ln 2 −Θ(1). We give a randomized algorithm which refutes the Super-Strong ETH for the case of random k-SAT and planted k-SAT for any clause-to-variable ratio. In particular, given any random k-SAT instance F with n variables and m clauses, our algorithm decides satisfiability for F in 2n(1−Ω( log k)/k) time, with high probability (over the choice of the formula and the randomness of the algorithm). It turns out that a well-known algorithm from the literature on SAT algorithms does the job: the PPZ algorithm of Paturi, Pudlak, and Zane (1998).2. The Unique k-SAT problem is the special case where there is at most one satisfying assignment. It is natural to hypothesize that the worst-case (exponential-time) complexity of Unique k-SAT is substantially less than that of k-SAT. Improving prior reductions, we show the time complexities of Unique k-SAT and k-SAT are very tightly related: if Unique k-SAT is in 2n(1−f(k)/k) time for an unbounded f, then k-SAT is in 2n(1−f(k)(1−ɛ)/k) time for every ɛ > 0. Thus, refuting Super Strong ETH in the unique solution case would refute Super Strong ETH in general.

Applications

2004 ◽  
Author(s):  
Wesley Petersen ◽  
Peter Arbenz
Keyword(s):  
Linear Algebra ◽  
Sparse Matrix ◽  
Sparse Matrices ◽  
Simple Procedure ◽  
Complex Numbers ◽  
Multiprocessor Computer ◽  
Basic Linear Algebra Subprograms ◽  
The Matrix ◽  
Shared Memory Multiprocessor ◽  
Real Complex

Linear algebra is often the kernel of most numerical computations. It deals with vectors and matrices and simple operations like addition and multiplication on these objects. Vectors are one-dimensional arrays of say n real or complex numbers x0, x1, . . . , xn−1. We denote such a vector by x and think of it as a column vector, On a sequential computer, these numbers occupy n consecutive memory locations. This is also true, at least conceptually, on a shared memory multiprocessor computer. On distributed memory multicomputers, the primary issue is how to distribute vectors on the memory of the processors involved in the computation. Matrices are two-dimensional arrays of the form The n · m real (complex) matrix elements aij are stored in n · m (respectively 2 · n ·m if complex datatype is available) consecutive memory locations. This is achieved by either stacking the columns on top of each other or by appending row after row. The former is called column-major, the latter row-major order. The actual procedure depends on the programming language. In Fortran, matrices are stored in column-major order, in C in row-major order. There is no principal difference, but for writing efficient programs one has to respect how matrices are laid out. To be consistent with the libraries that we will use that are mostly written in Fortran, we will explicitly program in column-major order. Thus, the matrix element aij of the m×n matrix A is located i+j · m memory locations after a00. Therefore, in our C codes we will write a[i+j*m]. Notice that there is no such simple procedure for determining the memory location of an element of a sparse matrix. In Section 2.3, we outline data descriptors to handle sparse matrices. In this and later chapters we deal with one of the simplest operations one wants to do with vectors and matrices: the so-called saxpy operation (2.3). In Tables 2.1 and 2.2 are listed some of the acronyms and conventions for the basic linear algebra subprograms discussed in this book.

Quantum algebras and parity-dependent spectra

2007 ◽  
Vol 463 (2086) ◽  
pp. 2415-2427 ◽  
Author(s):  
C.V Sukumar ◽  
Andrew Hodges
Keyword(s):  
Quantum Optics ◽  
Harmonic Oscillator ◽  
Combinatorial Theory ◽  
Squeezed States ◽  
Quantum Algebras ◽  
Matrix Elements ◽  
Euler Numbers ◽  
Simple Harmonic Oscillator ◽  
Non Gaussian ◽  
Special Case

We study the structure of a quantum algebra in which a parity-violating term modifies the standard commutation relation between the creation and annihilation operators of the simple harmonic oscillator. We discuss several useful applications of the modified algebra. We show that the Bernoulli and Euler numbers arise naturally in a special case. We also show a connection with Gaussian and non-Gaussian squeezed states of the simple harmonic oscillator. Such states have been considered in quantum optics. The combinatorial theory of Bernoulli and Euler numbers is developed and used to calculate matrix elements for squeezed states.

Hybrid Complex Numbers: The Matrix Version

2018 ◽  
Vol 28 (3) ◽  
Author(s):  
G. Dattoli ◽  
S. Licciardi ◽  
R. M. Pidatella ◽  
E. Sabia
Keyword(s):  
Complex Numbers ◽  
Matrix Version ◽  
Hybrid Complex ◽  
The Matrix

scholarly journals Construction of generalized rotations and quasi-orthogonal matrices

2019 ◽  
Vol 7 (1) ◽  
pp. 107-113
Author(s):  
Luis Verde-Star
Keyword(s):  
Image Processing ◽  
Hadamard Matrices ◽  
Complex Numbers ◽  
Data Communications ◽  
Matrix Representations ◽  
Orthogonal Matrices ◽  
Orthogonal Designs ◽  
The Matrix ◽  
The One

Abstract We propose some methods for the construction of large quasi-orthogonal matrices and generalized rotations that may be used in applications in data communications and image processing. We use certain combinations of constructions by blocks similar to the one used by Sylvester to build Hadamard matrices. The orthogonal designs related with the matrix representations of the complex numbers, the quaternions, and the octonions are used in our construction procedures.

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