On the solution of the fuzzy Sylvester matrix equation

In this paper, we consider the fuzzy Sylvester matrix equation AX+XB=C,AX+XB=C, where A ? \mathbbR^{n ×n}A\in {\mathbb{R}}^{n \times n} and B ? \mathbbR^{m ×m}B\in {\mathbb{R}}^{m \times m} are crisp M-matrices, C is an n×mn\times m fuzzy matrix and X is unknown. We first transform this system to an (mn)×(mn)(mn)\times (mn) fuzzy system of linear equations. Then, we investigate the existence and uniqueness of a fuzzy solution to this system. We use the accelerated over-relaxation method to compute an approximate solution to this system. Some numerical experiments are given to illustrate the theoretical results.     

Characterizing sets of jobs that admit optimal greedy-like algorithms

The “Priority Algorithm” is a model of computation introduced by Borodin, Nielsen and Rackoff ((Incremental) Priority algorithms, Algorithmica 37(4):295–326, 2003) which formulates a wide class of greedy algorithms. For an arbitrary set \mathbbS\mathbb{S} of jobs, we are interested in whether or not there exists a priority algorithm that gains optimal profit on every subset of \mathbbS\mathbb{S} . In the case where the jobs are all intervals, we characterize such sets \mathbbS\mathbb{S} and give an efficient algorithm (when \mathbbS\mathbb{S} is finite) for determining this. We show that in general, however, the problem is NP-hard.     

Interpolation of Shifted-Lacunary Polynomials

Given a “black box” function to evaluate an unknown rational polynomial f ? \mathbbQ[x]f \in {\mathbb{Q}}[x] at points modulo a prime p, we exhibit algorithms to compute the representation of the polynomial in the sparsest shifted power basis. That is, we determine the sparsity $t \in {\mathbb{Z}}_{>0}$t \in {\mathbb{Z}}_{>0}, the shift a ? \mathbbQ\alpha \in {\mathbb{Q}}, the exponents 0 ￡ e₁ < e₂ < ? < e_t{0 \leq e_{1} < e_{2} < \cdots < e_{t}}, and the coefficients c₁, ?, c_t ? \mathbbQ \{0}c_{1}, \ldots , c_{t} \in {\mathbb{Q}} \setminus \{0\} such that

Lower Bounds for Agnostic Learning via Approximate Rank

We prove that the concept class of disjunctions cannot be pointwise approximated by linear combinations of any small set of arbitrary real-valued functions. That is, suppose that there exist functions f₁, ?, f_r\phi_{1}, \ldots , \phi_{r} : {− 1, 1}ⁿ → \mathbbR{\mathbb{R}} with the property that every disjunction f on n variables has $\|f - \sum\nolimits_{i=1}^{r} \alpha_{i}\phi _{i}\|_{\infty}\leq 1/3$\|f - \sum\nolimits_{i=1}^{r} \alpha_{i}\phi _{i}\|_{\infty}\leq 1/3 for some reals a₁, ?, a_r\alpha_{1}, \ldots , \alpha_{r}. We prove that then $r \geq exp \{\Omega(\sqrt{n})\}$r \geq exp \{\Omega(\sqrt{n})\}, which is tight. We prove an incomparable lower bound for the concept class of decision lists. For the concept class of majority functions, we obtain a lower bound of W(2ⁿ/n)\Omega(2^{n}/n) , which almost meets the trivial upper bound of 2ⁿ for any concept class. These lower bounds substantially strengthen and generalize the polynomial approximation lower bounds of Paturi (1992) and show that the regression-based agnostic learning algorithm of Kalai et al. (2005) is optimal.     

Tripartite entanglement sudden death in Yang-Baxter systems

In this paper, we derive unitary Yang-Baxter \breveR(q, j){\breve{R}(\theta, \varphi)} matrices from the 8×8 \mathbbM{8\times8\,\mathbb{M}} matrix and the 4 × 4 M matrix by Yang-Baxteration approach, where \mathbbM/M{\mathbb{M}/M} is the image of the braid group representation. In Yang-Baxter systems, we explore the evolution of tripartite negativity for three qubits Greenberger-Horne-Zeilinger (GHZ)-type states and W-type states and investigate the evolution of the bipartite concurrence for 2 qubits Bell-type states. We show that tripartite entanglement sudden death (ESD) and bipartite ESD all can happen in Yang-Baxter systems and find that ESD all are sensitive to the initial condition. Interestingly, we find that in the Yang-Baxter system, GHZ-type states can have bipartite entanglement and bipartite ESD, and find that in some initial conditions, W-type states have tripartite ESD while they have no bipartite Entanglement. It is worth noting that the meaningful parameter j{\varphi} has great influence on bipartite ESD for two qubits Bell-type states in the Yang-Baxter system.     

New Results on Noncommutative and Commutative Polynomial Identity Testing

Using ideas from automata theory, we design the first polynomial deterministic identity testing algorithm for the sparse noncommutative polynomial identity testing problem. Given a noncommuting black-box polynomial f ? \mathbb F{x₁,?,x_n}f \in {\mathbb F}\{x_{1},\ldots,x_n\} of degree d with at most t monomials, where the variables x_i are noncommuting, we give a deterministic polynomial identity test that checks if C o 0C \equiv 0 and runs in time polynomial in d, n, |C|, and t. Our algorithm evaluates the black-box polynomial for x_i assigned to matrices over \mathbbF{\mathbb{F}} and, in fact, reconstructs the entire polynomial f in time polynomial in n, d and t.     

New Approximation Algorithms for Minimum Cycle Bases of Graphs

We consider the problem of computing an approximate minimum cycle basis of an undirected non-negative edge-weighted graph G with m edges and n vertices; the extension to directed graphs is also discussed. In this problem, a {0,1} incidence vector is associated with each cycle and the vector space over \mathbbF₂\mathbb{F}_{2} generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of the weights of the cycles is minimum is called a minimum cycle basis of G.     

Multivariate numerical approximation using constructive L^{2} (\mathbb{R}) RBF neural network

For the multivariate continuous function, using constructive feedforward L² (\mathbbR) L^{2} (\mathbb{R}) radial basis function (RBF) neural network, we prove that a L² (\mathbbR) L^{2} (\mathbb{R}) RBF neural network with n + 1 hidden neurons can interpolate n + 1 multivariate samples with zero error. Then, we prove that the L² (\mathbbR) L^{2} (\mathbb{R}) RBF neural network can uniformly approximate any continuous multivariate function with arbitrary precision. The correctness and effectiveness are verified through eight numeric experiments.     

On the efficiency of quantum algorithms for Hamiltonian simulation

We study algorithms simulating a system evolving with Hamiltonian H = ?_j=1^m H_j{H = \sum_{j=1}^m H_j} , where each of the H _j , j = 1, . . . ,m, can be simulated efficiently. We are interested in the cost for approximating e^-iHt, t ? \mathbbR{e^{-iHt}, t \in \mathbb{R}} , with error e{\varepsilon} . We consider algorithms based on high order splitting formulas that play an important role in quantum Hamiltonian simulation. These formulas approximate e ^−iHt by a product of exponentials involving the H _j , j = 1, . . . ,m. We obtain an upper bound for the number of required exponentials. Moreover, we derive the order of the optimal splitting method that minimizes our upper bound. We show significant speedups relative to previously known results.     

On the complexity of binary samples

Consider a class of binary functions h: X→{ − 1, + 1} on an interval . Define the sample width of h on a finite subset (a sample) S ⊂ X as ω _S (h) =  min _x ∈ S |ω _h (x)| where ω _h (x) = h(x) max {a ≥ 0: h(z) = h(x), x − a ≤ z ≤ x + a}. Let be the space of all samples in X of cardinality ℓ and consider sets of wide samples, i.e., hypersets which are defined as Through an application of the Sauer-Shelah result on the density of sets an upper estimate is obtained on the growth function (or trace) of the class , β > 0, i.e., on the number of possible dichotomies obtained by intersecting all hypersets with a fixed collection of samples of cardinality m. The estimate is .      

Quantitative Separation Logic and Programs with Lists

This paper presents an extension of a decidable fragment of Separation Logic for singly-linked lists, defined by Berdine et al. (2004). Our main extension consists in introducing atomic formulae of the form ls ^k (x, y) describing a list segment of length k, stretching from x to y, where k is a logical variable interpreted over positive natural numbers, that may occur further inside Presburger constraints. We study the decidability of the full first-order logic combining unrestricted quantification of arithmetic and location variables. Although the full logic is found to be undecidable, validity of entailments between formulae with the quantifier prefix in the language $^* {$_\mathbbN, "_\mathbbN}^*\exists^* \{\exists_{\bf \mathbb{N}}, \forall_{\bf \mathbb{N}}\}^* is decidable. We provide here a model theoretic method, based on a parametric notion of shape graphs. We have implemented our decision technique, providing a fully automated framework for the verification of quantitative properties expressed as pre- and post-conditions on programs working on lists and integer counters.     

Lower estimation of approximation rate for neural networks

Let SFd and Πψ,n,d = { nj=1bjψ(ωj·x+θj) :bj,θj∈R,ωj∈Rd} be the set of periodic and Lebesgue’s square-integrable functions and the set of feedforward neural network (FNN) functions, respectively. Denote by dist (SF d, Πψ,n,d) the deviation of the set SF d from the set Πψ,n,d. A main purpose of this paper is to estimate the deviation. In particular, based on the Fourier transforms and the theory of approximation, a lower estimation for dist (SFd, Πψ,n,d) is proved. That is, dist(SF d, Πψ,n,d) (nlogC2n)1/2 . T...     

Outer Interval Solution of the Eigenvalue Problem under General Form Parametric Dependencies

The paper addresses the problem of determining an outer interval solution of the parametric eigenvalue problem A(p)x = λx, A(p) ∈ ℝ^n×n for the general case where the matrix elements a_ij(p) are continuous nonlinear functions of the parameter vector p, p belonging to the interval vector p. A method for computing an interval enclosure of each eigenpair (λ_μ, x^(μ)), μ = 1, ..., n, is suggested for the case where λ_μ is a simple eigenvalue. It is based on the use of an affine interval approximation of a_ij(p) in p and reduces, essentially, to setting up and solving a real system of n or 2n incomplete quadratic equations for each real or complex eigenvalue, respectively.     

Cardinal interpolation with periodic polysplines on strips

Abstract  We obtain a multivariate extension of a classical result of Schoenberg on cardinal spline interpolation. Specifically, we prove the existence of a unique function in , polyharmonic of order p on each strip , , and periodic in its last n variables, whose restriction to the parallel hyperplanes , , coincides with a prescribed sequence of n-variate periodic data functions satisfying a growth condition in . The constructive proof is based on separation of variables and on Micchelli’s theory of univariate cardinal -splines. Keywords: cardinal -splines, polyharmonic functions, multivariable interpolation Mathematics Subject Classification (2000): 41A05, 41A15, 41A63     

Image Approximation by Rectangular Wavelet Transform

We study image approximation by a separable wavelet basis and ϕ,ψ are elements of a standard biorthogonal wavelet basis in L₂(ℝ). Because k₁≠ k₂, the supports of the basis elements are rectangles, and the corresponding transform is known as the rectangular wavelet transform. We provide a self-contained proof that if one-dimensional wavelet basis has M dual vanishing moments then the rate of approximation by N coefficients of rectangular wavelet transform is for functions with mixed derivative of order M in each direction. These results are consistent with optimal approximation rates for such functions. The square wavelet transform yields the approximation rate is for functions with all derivatives of the total order M. Thus, the rectangular wavelet transform can outperform the square one if an image has a mixed derivative. We provide experimental comparison of image approximation which shows that rectangular wavelet transform outperform the square one. Vyacheslav Zavadsky got his M.Sc. (with distinction) in computer science and applied mathematics from Belarusian State University in 1994 and his Ph.D. in mathematics and statistics in 1998 from Belarusian Academy of Sciences and Belarusian State University. He worked at Institute of Mathematics of Belarusian Academy of sciences, and Belarusian center for medical technologies. He also held progressively responsible technical and research positions in the industry: at MZOR, eBusiness technologies, and Webmotion. At present, he is the principal software architect with Semiconductor insights. His research interests include mathematical and statistical methods in vision; machine learning, and structural data mining. Vyacheslav is author of more then ten peer reviewed papers and conference presentation, and 7 pending inventions.     

Communication Complexity Under Product and Nonproduct Distributions

We solve an open problem in communication complexity posed by Kushilevitz and Nisan (1997). Let R_∈(f) and $D^\mu_\in (f)$D^\mu_\in (f) denote the randomized and μ-distributional communication complexities of f, respectively (∈ a small constant). Yao’s well-known minimax principle states that $R_{\in}(f) = max_\mu \{D^\mu_\in(f)\}$R_{\in}(f) = max_\mu \{D^\mu_\in(f)\}. Kushilevitz and Nisan (1997) ask whether this equality is approximately preserved if the maximum is taken over product distributions only, rather than all distributions μ. We give a strong negative answer to this question. Specifically, we prove the existence of a function f : {0, 1}ⁿ ×{0, 1}ⁿ ? {0, 1}f : \{0, 1\}^n \times \{0, 1\}^n \rightarrow \{0, 1\} for which max_{μ product} {D^m _? (f)} = Q(1)  but R _? (f) = Q(n)\{D^\mu_\in (f)\} = \Theta(1) \,{\textrm but}\, R_{\in} (f) = \Theta(n). We also obtain an exponential separation between the statistical query dimension and signrank, solving a problem previously posed by the author (2007).     

Spectrum of the density matrix of a large block of spins of the XY model in one dimension

We consider reduced density matrix of a large block of consecutive spins in the ground states of the XY spin chain on an infinite lattice. We derive the spectrum of the density matrix using expression of Rényi entropy in terms of modular functions. The eigenvalues λ _n form exact geometric sequence. For example, for strong magnetic field λ _n = C exp(−π τ ₀ n), here τ ₀ > 0 and C > 0 depend on anisotropy and magnetic field. Different eigenvalues are degenerated differently. The largest eigenvalue is unique, but degeneracy g _n increases sub-exponentially as eigenvalues diminish: g_n ~ exp(p?{n/3}){g_{n}\sim \exp{(\pi \sqrt{n/3})}}. For weak magnetic field expressions are similar.     

Numerical solutions of AXB = C for mirrorsymmetric matrix X under a specified submatrix constraint

Mirrorsymmetric matrices, which are the iteraction matrices of mirrorsymmetric structures, have important application in studying odd/even-mode decomposition of symmetric multiconductor transmission lines (MTL). In this paper we present an efficient algorithm for minimizing ${\|AXB-C\|}$ where ${\|\cdot\|}$ is the Frobenius norm, ${A\in \mathbb{R}^{m\times n}}$ , ${B\in \mathbb{R}^{n\times s}}$ , ${C\in \mathbb{R}^{m\times s}}$ and ${X\in \mathbb{R}^{n\times n}}$ is mirrorsymmetric with a specified central submatrix [x _ij ] _{r≤i, j≤n-r} . Our algorithm produces a suitable X such that AXB = C in finitely many steps, if such an X exists. We show that the algorithm is stable any case, and we give results of numerical experiments that support this claim.