84 research outputs found
Algorithm 950: Ncpol2sdpa---Sparse Semidefinite Programming Relaxations for Polynomial Optimization Problems of Noncommuting Variables
A hierarchy of semidefinite programming (SDP) relaxations approximates the
global optimum of polynomial optimization problems of noncommuting variables.
Generating the relaxation, however, is a computationally demanding task, and
only problems of commuting variables have efficient generators. We develop an
implementation for problems of noncommuting problems that creates the
relaxation to be solved by SDPA -- a high-performance solver that runs in a
distributed environment. We further exploit the inherent sparsity of
optimization problems in quantum physics to reduce the complexity of the
resulting relaxations. Constrained problems with a relaxation of order two may
contain up to a hundred variables. The implementation is available in Python.
The tool helps solve problems such as finding the ground state energy or
testing quantum correlations.Comment: 17 pages, 3 figures, 1 table, 2 algorithms, the algorithm is
available at http://peterwittek.github.io/ncpol2sdpa
Characterizing finite-dimensional quantum behavior
We study and extend the semidefinite programming (SDP) hierarchies introduced
in [Phys. Rev. Lett. 115, 020501] for the characterization of the statistical
correlations arising from finite dimensional quantum systems. First, we
introduce the dimension-constrained noncommutative polynomial optimization
(NPO) paradigm, where a number of polynomial inequalities are defined and
optimization is conducted over all feasible operator representations of bounded
dimensionality. Important problems in device independent and semi-device
independent quantum information science can be formulated (or almost
formulated) in this framework. We present effective SDP hierarchies to attack
the general dimension-constrained NPO problem (and related ones) and prove
their asymptotic convergence. To illustrate the power of these relaxations, we
use them to derive new dimension witnesses for temporal and Bell-type
correlation scenarios, and also to bound the probability of success of quantum
random access codes.Comment: 17 page
ON THE COMPLEXITY OF SEMIDEFINITE PROGRAMS ARISING IN POLYNOMIAL OPTIMIZATION
In this paper we investigate matrix inequalities which hold irrespective of the size of the matrices involved, and explain how the search for such inequalities can be implemented as a semidefinite program (SDP). We provide a comprehensive discussion of the time complexity of these SDPs
Matrix Convex Hulls of Free Semialgebraic Sets
This article resides in the realm of the noncommutative (free) analog of real
algebraic geometry - the study of polynomial inequalities and equations over
the real numbers - with a focus on matrix convex sets and their projections
. A free semialgebraic set which is convex as well as bounded and open
can be represented as the solution set of a Linear Matrix Inequality (LMI), a
result which suggests that convex free semialgebraic sets are rare. Further,
Tarski's transfer principle fails in the free setting: The projection of a free
convex semialgebraic set need not be free semialgebraic. Both of these results,
and the importance of convex approximations in the optimization community,
provide impetus and motivation for the study of the free (matrix) convex hull
of free semialgebraic sets.
This article presents the construction of a sequence of LMI domains
in increasingly many variables whose projections are
successively finer outer approximations of the matrix convex hull of a free
semialgebraic set . It is based on free analogs of
moments and Hankel matrices. Such an approximation scheme is possibly the best
that can be done in general. Indeed, natural noncommutative transcriptions of
formulas for certain well known classical (commutative) convex hulls does not
produce the convex hulls in the free case. This failure is illustrated on one
of the simplest free nonconvex .
A basic question is which free sets are the projection of a free
semialgebraic set ? Techniques and results of this paper bear upon this
question which is open even for convex sets.Comment: 41 pages; includes table of contents; supplementary material (a
Mathematica notebook) can be found at
http://www.math.auckland.ac.nz/~igorklep/publ.htm
The convex Positivstellensatz in a free algebra
Given a monic linear pencil L in g variables let D_L be its positivity
domain, i.e., the set of all g-tuples X of symmetric matrices of all sizes
making L(X) positive semidefinite. Because L is a monic linear pencil, D_L is
convex with interior, and conversely it is known that convex bounded
noncommutative semialgebraic sets with interior are all of the form D_L. The
main result of this paper establishes a perfect noncommutative
Nichtnegativstellensatz on a convex semialgebraic set. Namely, a noncommutative
polynomial p is positive semidefinite on D_L if and only if it has a weighted
sum of squares representation with optimal degree bounds: p = s^* s + \sum_j
f_j^* L f_j, where s, f_j are vectors of noncommutative polynomials of degree
no greater than 1/2 deg(p). This noncommutative result contrasts sharply with
the commutative setting, where there is no control on the degrees of s, f_j and
assuming only p nonnegative, as opposed to p strictly positive, yields a clean
Positivstellensatz so seldom that such cases are noteworthy.Comment: 22 page
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