15 research outputs found
On the complexity of Putinar's Positivstellensatz
We prove an upper bound on the degree complexity of Putinar's
Positivstellensatz. This bound is much worse than the one obtained previously
for Schm\"udgen's Positivstellensatz but it depends on the same parameters. As
a consequence, we get information about the convergence rate of Lasserre's
procedure for optimization of a polynomial subject to polynomial constraints
Copositive certificates of non-negativity for polynomials on semialgebraic sets
A certificate of non-negativity is a way to write a given function so that
its non-negativity becomes evident. Certificates of non-negativity are
fundamental tools in optimization, and they underlie powerful algorithmic
techniques for various types of optimization problems. We propose certificates
of non-negativity of polynomials based on copositive polynomials. The
certificates we obtain are valid for generic semialgebraic sets and have a
fixed small degree, while commonly used sums-of-squares (SOS) certificates are
guaranteed to be valid only for compact semialgebraic sets and could have large
degree. Optimization over the cone of copositive polynomials is not tractable,
but this cone has been well studied. The main benefit of our copositive
certificates of non-negativity is their ability to translate results known
exclusively for copositive polynomials to more general semialgebraic sets. In
particular, we show how to use copositive polynomials to construct structured
(e.g., sparse) certificates of non-negativity, even for unstructured
semialgebraic sets. Last but not least, copositive certificates can be used to
obtain not only hierarchies of tractable lower bounds, but also hierarchies of
tractable upper bounds for polynomial optimization problems.Comment: 27 pages, 1 figur
Complexity in Automation of SOS Proofs: An Illustrative Example
We present a case study in proving invariance
for a chaotic dynamical system, the logistic map, based on
Positivstellensatz refutations, with the aim of studying the
problems associated with developing a completely automated
proof system. We derive the refutation using two different forms
of the Positivstellensatz and compare the results to illustrate the
challenges in defining and classifying the âcomplexityâ of such
a proof. The results show the flexibility of the SOS framework
in converting a dynamics problem into a semialgebraic one as
well as in choosing the form of the proof. Yet it is this very
flexibility that complicates the process of automating the proof
system and classifying proof âcomplexity.
Polynomial Optimization with Real Varieties
We consider the optimization problem of minimizing a polynomial f(x) subject
to polynomial constraints h(x)=0, g(x)>=0. Lasserre's hierarchy is a sequence
of sum of squares relaxations for finding the global minimum. Let K be the
feasible set. We prove the following results: i) If the real variety V_R(h) is
finite, then Lasserre's hierarchy has finite convergence, no matter the complex
variety V_C(h) is finite or not. This solves an open question in Laurent's
survey. ii) If K and V_R(h) have the same vanishing ideal, then the finite
convergence of Lasserre's hierarchy is independent of the choice of defining
polynomials for the real variety V_R(h). iii) When K is finite, a refined
version of Lasserre's hierarchy (using the preordering of g) has finite
convergence.Comment: 12 page
Conic Optimization Theory: Convexification Techniques and Numerical Algorithms
Optimization is at the core of control theory and appears in several areas of
this field, such as optimal control, distributed control, system
identification, robust control, state estimation, model predictive control and
dynamic programming. The recent advances in various topics of modern
optimization have also been revamping the area of machine learning. Motivated
by the crucial role of optimization theory in the design, analysis, control and
operation of real-world systems, this tutorial paper offers a detailed overview
of some major advances in this area, namely conic optimization and its emerging
applications. First, we discuss the importance of conic optimization in
different areas. Then, we explain seminal results on the design of hierarchies
of convex relaxations for a wide range of nonconvex problems. Finally, we study
different numerical algorithms for large-scale conic optimization problems.Comment: 18 page
Approximate volume and integration for basic semi-algebraic sets
Given a basic compact semi-algebraic set \K\subset\R^n, we introduce a
methodology that generates a sequence converging to the volume of \K. This
sequence is obtained from optimal values of a hierarchy of either semidefinite
or linear programs. Not only the volume but also every finite vector of moments
of the probability measure that is uniformly distributed on \K can be
approximated as closely as desired, and so permits to approximate the integral
on \K of any given polynomial; extension to integration against some weight
functions is also provided. Finally, some numerical issues associated with the
algorithms involved are briefly discussed