Topologies for which every nonzero vector is hypercyclic

2018 ◽  
Vol 80 (1) ◽  
pp. 3-24
Author(s):  
Henrik Petersson
Keyword(s):  
2019 ◽  
Vol 7 (1) ◽  
pp. 291-303
Author(s):  
Megan Wendler

Abstract A (strictly) semimonotone matrix A ∈ ℝn×n is such that for every nonzero vector x ∈ ℝn with nonnegative entries, there is an index k such that xk > 0 and (Ax)k is nonnegative (positive). A matrix which is (strictly) semimonotone has the property that every principal submatrix is also (strictly) semimonotone. Thus, it becomes natural to examine the almost (strictly) semimonotone matrices which are those matrices which are not (strictly) semimonotone but whose proper principal submatrices are (strictly) semimonotone. We characterize the 2 × 2 and 3 × 3 almost (strictly) semimonotone matrices and describe many of their properties. Then we explore general almost (strictly) semimonotone matrices, including the problem of detection and construction. Finally, we relate (strict) central matrices to semimonotone matrices.


2019 ◽  
Vol 29 (14) ◽  
pp. 1950201 ◽  
Author(s):  
Antonio Bonilla ◽  
Marko Kostić

If we change the upper and lower densities in the definition of distributional chaos of a continuous linear operator on a Banach space [Formula: see text] by the Banach upper and Banach lower densities, respectively, we obtain Li–Yorke chaos. Motivated by this, we introduce the notions of reiterative distributional chaos of types [Formula: see text], [Formula: see text] and [Formula: see text] for continuous linear operators on Banach spaces, which are characterized in terms of the existence of an irregular vector with additional properties. Moreover, we study its relations with other dynamical properties and present the conditions for the existence of a vector subspace [Formula: see text] of [Formula: see text], such that every nonzero vector in [Formula: see text] is both irregular for [Formula: see text] and distributionally near zero for [Formula: see text].


Author(s):  
Gerhard Oertel

The reader, even if familiar with vectors, will find it useful to work through this chapter because it introduces notation that will be used throughout this book. We will take vectors to be entities that possess magnitude, orientation, and sense in three-dimensional space. Graphically, we will represent them as arrows with the sense from tail to head, magnitude proportional to the length, and orientation indicated by the angles they form with a given set of reference directions. Two different kinds of symbol will be used to designate vectors algebraically, boldface letters (and the boldface number zero for a vector of zero magnitude), and subscripted letters to be introduced later. The first problems deal with simple vector geometry and its algebraic representation. Multiplying a vector by a scalar affects only its magnitude (length) without changing its direction. Problem 1. State the necessary and sufficient conditions for the three vectors A, B, and C to form a triangle. (Problems 1–9, 12–14, 19–23, and 25 from Sokolnikoff & Redheffer, 1958.) Problem 2. Given the sum S = A + B and the difference D = A – B, find A and B in terms of S and D (a) graphically and (b) algebraically. Problem 3. (a) State the unit vector a with the same direction as a nonzero vector A. (b) Let two nonzero vectors A and B issue from the same point, forming an angle between them; using the result of (a), find a vector that bisects this angle. Problem 4. Using vector methods, show that a line from one of the vertices of a parallelogram to the midpoint of one of the nonadjacent sides trisects one of the diagonals. Two vectors are said to form with each other two distinct products: a scalar, the dot product, and a vector, the cross product.


2021 ◽  
Vol 68 (3) ◽  
pp. 1-40
Author(s):  
Arnab Bhattacharyya ◽  
Édouard Bonnet ◽  
László Egri ◽  
Suprovat Ghoshal ◽  
Karthik C. S. ◽  
...  

The -Even Set problem is a parameterized variant of the Minimum Distance Problem of linear codes over , which can be stated as follows: given a generator matrix and an integer , determine whether the code generated by has distance at most , or, in other words, whether there is a nonzero vector such that has at most nonzero coordinates. The question of whether -Even Set is fixed parameter tractable (FPT) parameterized by the distance has been repeatedly raised in the literature; in fact, it is one of the few remaining open questions from the seminal book of Downey and Fellows [1999]. In this work, we show that -Even Set is W [1]-hard under randomized reductions. We also consider the parameterized -Shortest Vector Problem (SVP) , in which we are given a lattice whose basis vectors are integral and an integer , and the goal is to determine whether the norm of the shortest vector (in the norm for some fixed ) is at most . Similar to -Even Set, understanding the complexity of this problem is also a long-standing open question in the field of Parameterized Complexity. We show that, for any , -SVP is W [1]-hard to approximate (under randomized reductions) to some constant factor.


Author(s):  
Deepshikha ◽  
Jyoti

We show that for every nonzero vector [Formula: see text] in [Formula: see text], the discrete wave packet system [Formula: see text] constitutes a frame for the unitary space [Formula: see text]. An application of this result is given, where frame conditions cannot be derived from discrete wavelet systems in [Formula: see text]. The canonical dual of discrete wave packet frame is also discussed.


2018 ◽  
Vol 18 (3&4) ◽  
pp. 283-305
Author(s):  
Yanlin Chen ◽  
Kai-Min Chung ◽  
Ching-Yi Lai

A lattice is the integer span of some linearly independent vectors. Lattice problems have many significant applications in coding theory and cryptographic systems for their conjectured hardness. The Shortest Vector Problem (SVP), which asks to find a shortest nonzero vector in a lattice, is one of the well-known problems that are believed to be hard to solve, even with a quantum computer. In this paper we propose space-efficient classical and quantum algorithms for solving SVP. Currently the best time-efficient algorithm for solving SVP takes 2^{n+o(n)} time and 2^{n+o(n)} space. Our classical algorithm takes 2^{2.05n+o(n)} time to solve SVP and it requires only 2^{0.5n+o(n)} space. We then adapt our classical algorithm to a quantum version, which can solve SVP in time 2^{1.2553n+o(n)} with 2^{0.5n+o(n)} classical space and only poly(n) qubits.


1990 ◽  
Vol 01 (01) ◽  
pp. 83-90 ◽  
Author(s):  
SHOSHICHI KOBAYASHI

Given a complex space X, we shall define a new infinitesimal form [Formula: see text] of what is known as the Kobayashi pseudo-distance dx. At each point x of X, [Formula: see text] defines a pseudo-norm in the tangent space TxX; it satisfies the usual conditions for a norm except that [Formula: see text] may vanish for a nonzero vector υ. It turns out that the new pseudo-metric is the double dual of the old pseudo-metric Fx defined in [2] and [4]. This means that the indicatrix of [Formula: see text] is the convex hull of the indicatrix of Fx. In particular, [Formula: see text]. Advantages of [Formula: see text] over Fx are two-fold. First, [Formula: see text] satisfies the usual convexity condition, i.e., [Formula: see text]. (In [3] Lang calls Fx a semi-length function since it does not, in general, satisfy the convexity condition.) Second, it is defined on Zariski tangent spaces. It can be easily shown that [Formula: see text] is upper semicontinuous at nonsingular points of X. The upper semicontinuity for Fx is known also only in the nonsingular case. Although [Formula: see text], it can be shown, at least when X is nonsingular, that [Formula: see text] induces dx.


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