211,485 research outputs found
The Tracial Hahn-Banach Theorem, Polar Duals, Matrix Convex Sets, and Projections of Free Spectrahedra
This article investigates matrix convex sets and introduces their tracial
analogs which we call contractively tracial convex sets. In both contexts
completely positive (cp) maps play a central role: unital cp maps in the case
of matrix convex sets and trace preserving cp (CPTP) maps in the case of
contractively tracial convex sets. CPTP maps, also known as quantum channels,
are fundamental objects in quantum information theory.
Free convexity is intimately connected with Linear Matrix Inequalities (LMIs)
L(x) = A_0 + A_1 x_1 + ... + A_g x_g > 0 and their matrix convex solution sets
{ X : L(X) is positive semidefinite }, called free spectrahedra. The
Effros-Winkler Hahn-Banach Separation Theorem for matrix convex sets states
that matrix convex sets are solution sets of LMIs with operator coefficients.
Motivated in part by cp interpolation problems, we develop the foundations of
convex analysis and duality in the tracial setting, including tracial analogs
of the Effros-Winkler Theorem.
The projection of a free spectrahedron in g+h variables to g variables is a
matrix convex set called a free spectrahedrop. As a class, free spectrahedrops
are more general than free spectrahedra, but at the same time more tractable
than general matrix convex sets. Moreover, many matrix convex sets can be
approximated from above by free spectrahedrops. Here a number of fundamental
results for spectrahedrops and their polar duals are established. For example,
the free polar dual of a free spectrahedrop is again a free spectrahedrop. We
also give a Positivstellensatz for free polynomials that are positive on a free
spectrahedrop.Comment: v2: 56 pages, reworked abstract and intro to emphasize the convex
duality aspects; v1: 60 pages; includes an index and table of content
Sharp identification regions in games
We study identification in static, simultaneous move finite games of complete information, where the presence of multiple Nash equilibria may lead to partial identification of the model parameters. The identification regions for these parameters proposed in the related literature are known not to be sharp. Using the theory of random sets, we show that the sharp identification region can be obtained as the set of minimizers of the distance from the conditional distribution of game's outcomes given covariates, to the conditional Aumann expectation given covariates of a properly defined random set. This is the random set of probability distributions over action profiles given profit shifters implied by mixed strategy Nash equilibria. The sharp identification region can be approximated arbitrarily accurately through a finite number of moment inequalities based on the support function of the conditional Aumann expectation. When only pure strategy Nash equilibria are played, the sharp identification region is exactly determined by a finite number of moment inequalities. We discuss how our results can be extended to other solution concepts, such as for example correlated equilibrium or rationality and rationalizability. We show that calculating the sharp identification region using our characterization is computationally feasible. We also provide a simple algorithm which finds the set of inequalities that need to be checked in order to insure sharpness. We use examples analyzed in the literature to illustrate the gains in identification afforded by our method.Identification, Random Sets, Aumann Expectation, Support Function, Capacity Functional, Normal Form Games, Inequality Constraints.
Free Extreme points of free spectrahedrops and generalized free spectrahedra
Matrix convexity generalizes convexity to the dimension free setting and has
connections to many mathematical and applied pursuits including operator
theory, quantum information, noncommutative optimization, and linear control
systems. In the setting of classical convex sets, extreme points are central
objects which exhibit many important properties. For example, the Minkowski
theorem shows that any element of a closed bounded convex set can be expressed
as a convex combination of extreme points. Extreme points are also of great
interest in the dimension free setting of matrix convex sets; however, here the
situation requires more nuance.
In the dimension free setting, there are many different types of extreme
points. Of particular importance are free extreme points, a highly restricted
type of extreme point that is closely connected to the dilation theoretic
Arveson boundary. If free extreme points span a matrix convex set through
matrix convex combinations, then they satisfy a strong notion of minimality in
doing so. However, not all closed bounded matrix convex sets even have free
extreme points. Thus, a major goal is to determine which matrix convex sets are
spanned by their free extreme points.
Building on a recent work of J. W. Helton and the author which shows that
free spectrahedra, i.e., dimension free solution sets to linear matrix
inequalities, are spanned by their free extreme points, we establish two
additional classes of matrix convex sets which are the matrix convex hull of
their free extreme points. Namely, we show that closed bounded free
spectrahedrops, i.e, closed bounded projections of free spectrahedra, are the
span of their free extreme points. Furthermore, we show that if one considers
linear operator inequalities that have compact operator defining tuples, then
the resulting ``generalized" free spectrahedra are spanned by their free
extreme points.Comment: 34 page
The Inflation Technique for Causal Inference with Latent Variables
The problem of causal inference is to determine if a given probability
distribution on observed variables is compatible with some causal structure.
The difficult case is when the causal structure includes latent variables. We
here introduce the for tackling this problem. An
inflation of a causal structure is a new causal structure that can contain
multiple copies of each of the original variables, but where the ancestry of
each copy mirrors that of the original. To every distribution of the observed
variables that is compatible with the original causal structure, we assign a
family of marginal distributions on certain subsets of the copies that are
compatible with the inflated causal structure. It follows that compatibility
constraints for the inflation can be translated into compatibility constraints
for the original causal structure. Even if the constraints at the level of
inflation are weak, such as observable statistical independences implied by
disjoint causal ancestry, the translated constraints can be strong. We apply
this method to derive new inequalities whose violation by a distribution
witnesses that distribution's incompatibility with the causal structure (of
which Bell inequalities and Pearl's instrumental inequality are prominent
examples). We describe an algorithm for deriving all such inequalities for the
original causal structure that follow from ancestral independences in the
inflation. For three observed binary variables with pairwise common causes, it
yields inequalities that are stronger in at least some aspects than those
obtainable by existing methods. We also describe an algorithm that derives a
weaker set of inequalities but is more efficient. Finally, we discuss which
inflations are such that the inequalities one obtains from them remain valid
even for quantum (and post-quantum) generalizations of the notion of a causal
model.Comment: Minor final corrections, updated to match the published version as
closely as possibl
A Characterization of Lyapunov Inequalities for Stability of Switched Systems
We study stability criteria for discrete-time switched systems and provide a
meta-theorem that characterizes all Lyapunov theorems of a certain canonical
type. For this purpose, we investigate the structure of sets of LMIs that
provide a sufficient condition for stability. Various such conditions have been
proposed in the literature in the past fifteen years. We prove in this note
that a family of languagetheoretic conditions recently provided by the authors
encapsulates all the possible LMI conditions, thus putting a conclusion to this
research effort. As a corollary, we show that it is PSPACE-complete to
recognize whether a particular set of LMIs implies stability of a switched
system. Finally, we provide a geometric interpretation of these conditions, in
terms of existence of an invariant set.Comment: arXiv admin note: text overlap with arXiv:1201.322
Small Extended Formulation for Knapsack Cover Inequalities from Monotone Circuits
Initially developed for the min-knapsack problem, the knapsack cover
inequalities are used in the current best relaxations for numerous
combinatorial optimization problems of covering type. In spite of their
widespread use, these inequalities yield linear programming (LP) relaxations of
exponential size, over which it is not known how to optimize exactly in
polynomial time. In this paper we address this issue and obtain LP relaxations
of quasi-polynomial size that are at least as strong as that given by the
knapsack cover inequalities.
For the min-knapsack cover problem, our main result can be stated formally as
follows: for any , there is a -size LP relaxation with an integrality gap of at most ,
where is the number of items. Prior to this work, there was no known
relaxation of subexponential size with a constant upper bound on the
integrality gap.
Our construction is inspired by a connection between extended formulations
and monotone circuit complexity via Karchmer-Wigderson games. In particular,
our LP is based on -depth monotone circuits with fan-in~ for
evaluating weighted threshold functions with inputs, as constructed by
Beimel and Weinreb. We believe that a further understanding of this connection
may lead to more positive results complementing the numerous lower bounds
recently proved for extended formulations.Comment: 21 page
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