50 research outputs found
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
Guaranteed clustering and biclustering via semidefinite programming
Identifying clusters of similar objects in data plays a significant role in a
wide range of applications. As a model problem for clustering, we consider the
densest k-disjoint-clique problem, whose goal is to identify the collection of
k disjoint cliques of a given weighted complete graph maximizing the sum of the
densities of the complete subgraphs induced by these cliques. In this paper, we
establish conditions ensuring exact recovery of the densest k cliques of a
given graph from the optimal solution of a particular semidefinite program. In
particular, the semidefinite relaxation is exact for input graphs corresponding
to data consisting of k large, distinct clusters and a smaller number of
outliers. This approach also yields a semidefinite relaxation for the
biclustering problem with similar recovery guarantees. Given a set of objects
and a set of features exhibited by these objects, biclustering seeks to
simultaneously group the objects and features according to their expression
levels. This problem may be posed as partitioning the nodes of a weighted
bipartite complete graph such that the sum of the densities of the resulting
bipartite complete subgraphs is maximized. As in our analysis of the densest
k-disjoint-clique problem, we show that the correct partition of the objects
and features can be recovered from the optimal solution of a semidefinite
program in the case that the given data consists of several disjoint sets of
objects exhibiting similar features. Empirical evidence from numerical
experiments supporting these theoretical guarantees is also provided
Immobile indices and CQ-free optimality criteria for linear copositive programming problems
We consider problems of linear copositive programming where feasible sets consist of vectors
for which the quadratic forms induced by the corresponding linear matrix combinations
are nonnegative over the nonnegative orthant. Given a linear copositive problem, we define
immobile indices of its constraints and a normalized immobile index set. We prove that the
normalized immobile index set is either empty or can be represented as a union of a finite
number of convex closed bounded polyhedra. We show that the study of the structure of
this set and the connected properties of the feasible set permits to obtain new optimality
criteria for copositive problems. These criteria do not require the fulfillment of any additional
conditions (constraint qualifications or other). An illustrative example shows that the
optimality conditions formulated in the paper permit to detect the optimality of feasible
solutions for which the known sufficient optimality conditions are not able to do this. We
apply the approach based on the notion of immobile indices to obtain new formulations of
regularized primal and dual problems which are explicit and guarantee strong duality.publishe
New complexity analysis for primal-dual interior-point methods for self-scaled optimization problems
Local Quadratic Convergence of Polynomial-Time Interior-Point Methods for Conic Optimization Problems
In this paper, we establish a local quadratic convergence of polynomial-time interiorpoint methods for general conic optimization problems. The main structural property used in our analysis is the logarithmic homogeneity of self-concordant barrier functions. We propose new path-following predictor-corrector schemes which work only in the dual space. They are based on an easily computable gradient proximity measure, which ensures an automatic transformation of the global linear rate of convergence to the local quadratic one under some mild assumptions. Our step-size procedure for the predictor step is related to the maximum step size (the one that takes us to the boundary). It appears that in order to obtain local superlinear convergence, we need to tighten the neighborhood of the central path proportionally to the current duality gap
Discretization and Localization in Successive Convex Relaxation Methods for Nonconvex Quadratic Optimization Problems
. Based on the authors' previous work which established theoretical foundations of two, conceptual, successive convex relaxation methods, i.e., the SSDP (Successive Semidefinite Programming) Relaxation Method and the SSILP (Successive Semi-Infinite Linear Programming) Relaxation Method, this paper proposes their implementable variants for general quadratic optimization problems. These problems have a linear objective function c T x to be maximized over a nonconvex compact feasible region F described by a finite number of quadratic inequalities. We introduce two new techniques, "discretization" and "localization," into the SSDP and SSILP Relaxation Methods. The discretization technique makes it possible to approximate an infinite number of semi-infinite SDPs (or semi-infinite LPs) which appeared at each iteration of the original methods by a finite number of standard SDPs (or standard LPs) with a finite number of linear inequality constraints. We establish: ffl Given any open convex ..
The effects of grinding, soaking and cooking on the degradation of amygdalin of bitter apricot seeds
More than 650 metric tonnes of bitter apricot seeds are produced in Turkey per year as a by-product from the fruit canning industry. The seeds contain the toxic cyanogenic glycoside amygdalin in amounts up to around 150 µmol/g fresh weight. The effect of grinding, soaking and cooking on the degradation of amygdalin to prunasin, benzaldehyde cyanohydrin and HCN, has been studied, as has the release of these cyanides into the soaking water. Analysis for total cyanogenic potential (TCP), cyanogenic glycosides and non-glycosidic cyanogens were thus made on a number of differently processed seed batches. The parameters were: particle size, soaking time and temperature, the presence of a natural microflora, and the duration of cooking. Great reductions were obtained for all three values measured, i.e. from the initial TCP of 85 µmol/g and down to around 2-4µmol/g. However, none of the products obtained were considered safe for human consumption, i.e. a further microbiological detoxification must be added. © 1995.The financial support by NATO Collaborative Research Grant 92W is gratefully acknowledged. The authors thank Prof. D. G6ktan. Food Engineering Department. Ege Clniversity. for his conslruetive criticism during this investigation. -