Geometric Duality for Convex Vector Optimization Problems

Geometric duality theory for multiple objective linear programming problems turned out to be very useful for the development of efficient algorithms to generate or approximate the whole set of nondominated points in the outcome space. This article extends the geometric duality theory to convex vector optimization problems.Comment: 21 page

Empirical properties of duality theory

This research examines selected empirical properties of duality relationships. Monte Carlo experiments indicate that Hessian matrices estimated from the normalised unrestricted profit, restricted profit and production functions yield conflicting results in the presence of measurement error and low relative price variability. In particular, small amounts of measurement error in quantity variables can translate into large errors in uncompensated estimates calculated via restricted and unrestricted profit and production functions. These results emphasise the need for high quality data when estimating empirical models in order to accurately determine dual relationships implied by economic theory.Research Methods/ Statistical Methods,

An Algebraic Duality Theory for Multiplicative Unitaries

Multiplicative Unitaries are described in terms of a pair of commuting shifts of relative depth two. They can be generated from ambidextrous Hilbert spaces in a tensor C*-category. The algebraic analogue of the Takesaki-Tatsuuma Duality Theorem characterizes abstractly C*-algebras acted on by unital endomorphisms that are intrinsically related to the regular representation of a multiplicative unitary. The relevant C*-algebras turn out to be simple and indeed separable if the corresponding multiplicative unitaries act on a separable Hilbert space. A categorical analogue provides internal characterizations of minimal representation categories of a multiplicative unitary. Endomorphisms of the Cuntz algebra related algebraically to the grading are discussed as is the notion of braided symmetry in a tensor C*-category.Comment: one reference adde

On Lagrangian Duality in Vector Optimization. Applications to the linear case.

The paper deals with vector constrained extremum problems. A separation scheme is recalled; starting from it, a vector Lagrangian duality theory is developed. The linear duality due to Isermann can be embedded in this separation approach. Some classical applications are extended to the multiobjective framework in the linear case, exploiting the duality theory of Isermann.Vector Optimization, Separation, Image Space Analysis, Lagrangian Duality, Set-Valued Function.

Applications of duality theory to cousin complexes

We use the anti-equivalence between Cohen-Macaulay complexes and coherent sheaves on formal schemes to shed light on some older results and prove new results. We bring out the relations between a coherent sheaf M satisfying an S_2 condition and the lowest cohomology N of its "dual" complex. We show that if a scheme has a Gorenstein complex satisfying certain coherence conditions, then in a finite \'etale neighborhood of each point, it has a dualizing complex. If the scheme already has a dualizing complex, then we show that the Gorenstein complex must be a tensor product of a dualizing complex and a vector bundle of finite rank. We relate the various results in [S] on Cousin complexes to dual results on coherent sheaves on formal schemes.Comment: 40 pages. Substantially different from earlier version(s). To appear in Journal of Algebr

APPLICATIONS OF DUALITY THEORY TO AGRICULTURE

Research Methods/ Statistical Methods,