6 research outputs found
Universal Duality in Conic Convex Optimization
Given a primal-dual pair of linear programs, it is known that if their optimal values are viewed as lying on the extended real line, then the duality gap is zero, unless both problems are infeasible, in which case the optimal values are +infinity and -infinity. In contrast, for optimization problems over nonpolyhedral convex cones, a nonzero duality gap can exist even in the case where the primal and dual problems are both feasible. For a pair of dual conic convex programs, we provide simple conditions on the onstraint matricesand cone under which the duality gap is zero for every choice of linear objective function and ight-hand-side We refer to this property as niversal duality Our conditions possess the following properties: (i) they are necessary and sufficient, in the sense that if (and only if) they do not hold, the duality gap is nonzero for some linear objective function and ight-hand-side (ii) they are metrically and topologically generic; and (iii) they can be verified by solving a single conic convex program. As a side result, we also show that the feasible sets of a primal conic convex program and its dual cannot both be bounded, unless they are both empty, and we relate this to universal duality
Strong duality in conic linear programming: facial reduction and extended duals
The facial reduction algorithm of Borwein and Wolkowicz and the extended dual
of Ramana provide a strong dual for the conic linear program in the absence of any constraint qualification. The facial
reduction algorithm solves a sequence of auxiliary optimization problems to
obtain such a dual. Ramana's dual is applicable when (P) is a semidefinite
program (SDP) and is an explicit SDP itself. Ramana, Tuncel, and Wolkowicz
showed that these approaches are closely related; in particular, they proved
the correctness of Ramana's dual using certificates from a facial reduction
algorithm.
Here we give a clear and self-contained exposition of facial reduction, of
extended duals, and generalize Ramana's dual:
-- we state a simple facial reduction algorithm and prove its correctness;
and
-- building on this algorithm we construct a family of extended duals when
is a {\em nice} cone. This class of cones includes the semidefinite cone
and other important cones.Comment: A previous version of this paper appeared as "A simple derivation of
a facial reduction algorithm and extended dual systems", technical report,
Columbia University, 2000, available from
http://www.unc.edu/~pataki/papers/fr.pdf Jonfest, a conference in honor of
Jonathan Borwein's 60th birthday, 201
Bad semidefinite programs: they all look the same
Conic linear programs, among them semidefinite programs, often behave
pathologically: the optimal values of the primal and dual programs may differ,
and may not be attained. We present a novel analysis of these pathological
behaviors. We call a conic linear system {\em badly behaved} if the
value of is finite but the dual program has no
solution with the same value for {\em some} We describe simple and
intuitive geometric characterizations of badly behaved conic linear systems.
Our main motivation is the striking similarity of badly behaved semidefinite
systems in the literature; we characterize such systems by certain {\em
excluded matrices}, which are easy to spot in all published examples.
We show how to transform semidefinite systems into a canonical form, which
allows us to easily verify whether they are badly behaved. We prove several
other structural results about badly behaved semidefinite systems; for example,
we show that they are in in the real number model of computing.
As a byproduct, we prove that all linear maps that act on symmetric matrices
can be brought into a canonical form; this canonical form allows us to easily
check whether the image of the semidefinite cone under the given linear map is
closed.Comment: For some reason, the intended changes between versions 4 and 5 did
not take effect, so versions 4 and 5 are the same. So version 6 is the final
version. The only difference between version 4 and version 6 is that 2 typos
were fixed: in the last displayed formula on page 6, "7" was replaced by "1";
and in the 4th displayed formula on page 12 "A_1 - A_2 - A_3" was replaced by
"A_3 - A_2 - A_1
Methodik zur Integration von Vorwissen in die Modellbildung
This book describes how prior knowledge about dynamical systems and functions can be integrated in mathematical modelling. The first part comprises the transformation of the known properties into a mathematical model and the second part explains four approaches for solving the resulting constrained optimization problems. Numerous examples, tables and compilations complete the book
Methodik zur Integration von Vorwissen in die Modellbildung
Das Buch zeigt, wie Vorwissen über Eigenschaften dynamischer Systeme und über Funktionen in die mathematische Modellbildung integriert werden kann. Hierzu wird im ersten Teil der Arbeit das verbale Vorwissen mathematisch formuliert. Der zweite Teil beschreibt vier Zugängen, um die entstehenden restringierten Probleme zu lösen. Zahlreiche Beispiele, Tabellen und Zusammenstellungen vervollständigen das Buch
Universal Duality in Conic Convex Optimization
Given a primal-dual pair of linear programs, it is well known that if their optimal values are viewed as lying on the extended real line, then the duality gap is zero, unless both problems are infeasible, in which case the optimal values are + ∞ and −∞. In contrast, for optimization problems over nonpolyhedral convex cones, a nonzero duality gap can exist when either the primal or the dual is feasible. For a pair of dual conic convex programs, we provide simple conditions on the “constraint matrices ” and cone under which the duality gap is zero for every choice of linear objective function and constraint right-hand side. We refer to this property as “universal duality”. Our conditions possess the following properties: (i) they are necessary and sufficient, in the sense that if (and only if) they do not hold, the duality gap is nonzero for some linear objective function and constraint right-hand side; (ii) they are metrically and topologically generic; and (iii) they can be verified by solving a single conic convex program. We relate to universal duality the fact that the feasible sets of a primal convex program and its dual cannot both be bounded, unless they are both empty. Finally we illustrate our theory on a class of semidefinite programs that appear in control theory applications