There is no doubt that one of the most important domains of optimization theory is concave maximization, the most delicate problems of which are the duality theory and the necessary and sufficient optimality conditions. Duality theory for one-objective concave maximization is, how it is generally known, already completely developed. For vector optimization, however, the duality question is mote...

In the paper, we describe various applications of the closedness and duality theorems of [7] and [8]. First, the strong separability of a polyhedron and a linear image of a convex set is characterized. Then, it is shown how stability conditions (known from the generalized Fenchel-Rockafellar duality theory) can be reformulated as closedness conditions. Finally, we present a generalized Lagrange...

We describe a general technique for determining upper bounds on maximal values (or lower bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the nonanticipativity constraints that require decisions to depend only on the information available at the time a decision is made and impose a “penalty” that punishes violations of nonanticipativity. In applications, the h...

For boolean quadratic programming (BQP), we will show that there is no duality gap between the primal and dual problems under some conditions by using the classical Lagrangian duality. A benchmark generator is given to create random BQP problems which can be solved in polynomial time. Several numerical examples are generated to demonstrate the effectiveness of the proposed method.

In this section, we consider a convex optimization problem p * := min x f 0 are affine. We denote by D the domain of the problem (which is the intersection of the domains of all the functions involved), and by X ⊆ D its feasible set.

A p-norm surrogate constraint method is proposed in this paper for integer programming. A single surrogate constraint can be always constructed using p-norm such that the feasible sets in a surrogate relaxation and the primal problem match exactly. The p-norm surro-gate constraint method is thus guaranteed to succeed in identifying the optimal solution of the primal problem with zero duality ga...

In the first part of this study we have introduced six different multiobjective dual problems to a general multiobjective optimization problem, for which we presented weak as well as strong duality assertions. Afterwards, we derived some inclusion results for the image sets of three of these problems. The aim of this second part is to complete our investigations by studying the relations betwee...

We present duality invariant structure of the thermodynamic quantities of non-extreme black hole solutions of torodially compactified Type II (M theory) and heterotic string in five and four dimensions. These quantities are parameterized by duality invariant combinations of charges and the nonextremality parameter, which measures a deviation from the BPS-saturated limit. In particular, in D = 5...

Duality is an important notion for constrained optimization which provides a theoretical foundation for a number of constraint decomposition schemes such as separable programming and for deriving lower bounds in space decomposition algorithms such as branch and bound. However, the conventional duality theory has the fundamental limit that it leads to duality gaps for nonconvex optimization prob...

Triality theory is proved for a general unconstrained global optimization problem. The method adopted is simple but mathematically rigorous. Results show that if the primal problem and its canonical dual have the same dimension, the triality theory holds strongly in the tri-duality form as it was originally proposed. Otherwise, both the canonical min-max duality and the double-max duality still...