1,543 research outputs found
Quantum-proof randomness extractors via operator space theory
Quantum-proof randomness extractors are an important building block for
classical and quantum cryptography as well as device independent randomness
amplification and expansion. Furthermore they are also a useful tool in quantum
Shannon theory. It is known that some extractor constructions are quantum-proof
whereas others are provably not [Gavinsky et al., STOC'07]. We argue that the
theory of operator spaces offers a natural framework for studying to what
extent extractors are secure against quantum adversaries: we first phrase the
definition of extractors as a bounded norm condition between normed spaces, and
then show that the presence of quantum adversaries corresponds to a completely
bounded norm condition between operator spaces. From this we show that very
high min-entropy extractors as well as extractors with small output are always
(approximately) quantum-proof. We also study a generalization of extractors
called randomness condensers. We phrase the definition of condensers as a
bounded norm condition and the definition of quantum-proof condensers as a
completely bounded norm condition. Seeing condensers as bipartite graphs, we
then find that the bounded norm condition corresponds to an instance of a well
studied combinatorial problem, called bipartite densest subgraph. Furthermore,
using the characterization in terms of operator spaces, we can associate to any
condenser a Bell inequality (two-player game) such that classical and quantum
strategies are in one-to-one correspondence with classical and quantum attacks
on the condenser. Hence, we get for every quantum-proof condenser (which
includes in particular quantum-proof extractors) a Bell inequality that can not
be violated by quantum mechanics.Comment: v3: 34 pages, published versio
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
The affine rank minimization problem consists of finding a matrix of minimum
rank that satisfies a given system of linear equality constraints. Such
problems have appeared in the literature of a diverse set of fields including
system identification and control, Euclidean embedding, and collaborative
filtering. Although specific instances can often be solved with specialized
algorithms, the general affine rank minimization problem is NP-hard. In this
paper, we show that if a certain restricted isometry property holds for the
linear transformation defining the constraints, the minimum rank solution can
be recovered by solving a convex optimization problem, namely the minimization
of the nuclear norm over the given affine space. We present several random
ensembles of equations where the restricted isometry property holds with
overwhelming probability. The techniques used in our analysis have strong
parallels in the compressed sensing framework. We discuss how affine rank
minimization generalizes this pre-existing concept and outline a dictionary
relating concepts from cardinality minimization to those of rank minimization
Polynomial Norms
In this paper, we study polynomial norms, i.e. norms that are the
root of a degree- homogeneous polynomial . We first show
that a necessary and sufficient condition for to be a norm is for
to be strictly convex, or equivalently, convex and positive definite. Though
not all norms come from roots of polynomials, we prove that any
norm can be approximated arbitrarily well by a polynomial norm. We then
investigate the computational problem of testing whether a form gives a
polynomial norm. We show that this problem is strongly NP-hard already when the
degree of the form is 4, but can always be answered by testing feasibility of a
semidefinite program (of possibly large size). We further study the problem of
optimizing over the set of polynomial norms using semidefinite programming. To
do this, we introduce the notion of r-sos-convexity and extend a result of
Reznick on sum of squares representation of positive definite forms to positive
definite biforms. We conclude with some applications of polynomial norms to
statistics and dynamical systems
Lower Bounds on Complexity of Lyapunov Functions for Switched Linear Systems
We show that for any positive integer , there are families of switched
linear systems---in fixed dimension and defined by two matrices only---that are
stable under arbitrary switching but do not admit (i) a polynomial Lyapunov
function of degree , or (ii) a polytopic Lyapunov function with facets, or (iii) a piecewise quadratic Lyapunov function with
pieces. This implies that there cannot be an upper bound on the size of the
linear and semidefinite programs that search for such stability certificates.
Several constructive and non-constructive arguments are presented which connect
our problem to known (and rather classical) results in the literature regarding
the finiteness conjecture, undecidability, and non-algebraicity of the joint
spectral radius. In particular, we show that existence of an extremal piecewise
algebraic Lyapunov function implies the finiteness property of the optimal
product, generalizing a result of Lagarias and Wang. As a corollary, we prove
that the finiteness property holds for sets of matrices with an extremal
Lyapunov function belonging to some of the most popular function classes in
controls
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