617 research outputs found
Primal robustness and semidefinite cones
This paper reformulates and streamlines the core tools of robust stability
and performance for LTI systems using now-standard methods in convex
optimization. In particular, robustness analysis can be formulated directly as
a primal convex (semidefinite program or SDP) optimization problem using sets
of gramians whose closure is a semidefinite cone. This allows various
constraints such as structured uncertainty to be included directly, and
worst-case disturbances and perturbations constructed directly from the primal
variables. Well known results such as the KYP lemma and various scaled small
gain tests can also be obtained directly through standard SDP duality. To
readers familiar with robustness and SDPs, the framework should appear obvious,
if only in retrospect. But this is also part of its appeal and should enhance
pedagogy, and we hope suggest new research. There is a key lemma proving
closure of a grammian that is also obvious but our current proof appears
unnecessarily cumbersome, and a final aim of this paper is to enlist the help
of experts in robust control and convex optimization in finding simpler
alternatives.Comment: A shorter version submitted to CDC 1
Witnesses of causal nonseparability: an introduction and a few case studies
It was recently realised that quantum theory allows for so-called causally
nonseparable processes, which are incompatible with any definite causal order.
This was first suggested on a rather abstract level by the formalism of process
matrices, which only assumes that quantum theory holds locally in some
observers' laboratories, but does not impose a global causal structure; it was
then shown, on a more practical level, that the quantum switch---a new resource
for quantum computation that goes beyond causally ordered circuits---provided
precisely a physical example of a causally nonseparable process. To demonstrate
that a given process is causally nonseparable, we introduced in [Ara\'ujo et
al., New J. Phys. 17, 102001 (2015)] the concept of witnesses of causal
nonseparability. Here we present a shorter introduction to this concept, and
concentrate on some explicit examples to show how to construct and use such
witnesses in practice.Comment: 15 pages, 7 figure
Witnessing causal nonseparability
Our common understanding of the physical world deeply relies on the notion
that events are ordered with respect to some time parameter, with past events
serving as causes for future ones. Nonetheless, it was recently found that it
is possible to formulate quantum mechanics without any reference to a global
time or causal structure. The resulting framework includes new kinds of quantum
resources that allow performing tasks - in particular, the violation of causal
inequalities - which are impossible for events ordered according to a global
causal order. However, no physical implementation of such resources is known.
Here we show that a recently demonstrated resource for quantum computation -
the quantum switch - is a genuine example of "indefinite causal order". We do
this by introducing a new tool - the causal witness - which can detect the
causal nonseparability of any quantum resource that is incompatible with a
definite causal order. We show however that the quantum switch does not violate
any causal nequality.Comment: 15 + 12 pages, 5 figures. Published versio
A Newton-bracketing method for a simple conic optimization problem
For the Lagrangian-DNN relaxation of quadratic optimization problems (QOPs),
we propose a Newton-bracketing method to improve the performance of the
bisection-projection method implemented in BBCPOP [to appear in ACM Tran.
Softw., 2019]. The relaxation problem is converted into the problem of finding
the largest zero of a continuously differentiable (except at )
convex function such that if
and otherwise. In theory, the method generates lower
and upper bounds of both converging to . Their convergence is
quadratic if the right derivative of at is positive. Accurate
computation of is necessary for the robustness of the method, but it is
difficult to achieve in practice. As an alternative, we present a
secant-bracketing method. We demonstrate that the method improves the quality
of the lower bounds obtained by BBCPOP and SDPNAL+ for binary QOP instances
from BIQMAC. Moreover, new lower bounds for the unknown optimal values of large
scale QAP instances from QAPLIB are reported.Comment: 19 pages, 2 figure
Improving Efficiency and Scalability of Sum of Squares Optimization: Recent Advances and Limitations
It is well-known that any sum of squares (SOS) program can be cast as a
semidefinite program (SDP) of a particular structure and that therein lies the
computational bottleneck for SOS programs, as the SDPs generated by this
procedure are large and costly to solve when the polynomials involved in the
SOS programs have a large number of variables and degree. In this paper, we
review SOS optimization techniques and present two new methods for improving
their computational efficiency. The first method leverages the sparsity of the
underlying SDP to obtain computational speed-ups. Further improvements can be
obtained if the coefficients of the polynomials that describe the problem have
a particular sparsity pattern, called chordal sparsity. The second method
bypasses semidefinite programming altogether and relies instead on solving a
sequence of more tractable convex programs, namely linear and second order cone
programs. This opens up the question as to how well one can approximate the
cone of SOS polynomials by second order representable cones. In the last part
of the paper, we present some recent negative results related to this question.Comment: Tutorial for CDC 201
Improving compressed sensing with the diamond norm
In low-rank matrix recovery, one aims to reconstruct a low-rank matrix from a
minimal number of linear measurements. Within the paradigm of compressed
sensing, this is made computationally efficient by minimizing the nuclear norm
as a convex surrogate for rank.
In this work, we identify an improved regularizer based on the so-called
diamond norm, a concept imported from quantum information theory. We show that
-for a class of matrices saturating a certain norm inequality- the descent cone
of the diamond norm is contained in that of the nuclear norm. This suggests
superior reconstruction properties for these matrices. We explicitly
characterize this set of matrices. Moreover, we demonstrate numerically that
the diamond norm indeed outperforms the nuclear norm in a number of relevant
applications: These include signal analysis tasks such as blind matrix
deconvolution or the retrieval of certain unitary basis changes, as well as the
quantum information problem of process tomography with random measurements.
The diamond norm is defined for matrices that can be interpreted as order-4
tensors and it turns out that the above condition depends crucially on that
tensorial structure. In this sense, this work touches on an aspect of the
notoriously difficult tensor completion problem.Comment: 25 pages + Appendix, 7 Figures, published versio
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