In support set expansion sensitivity analysis, one concerns to find the range of parameter variation where the perturbed problem has an optimal solution with the support set that includes the support set of the given optimal solution of the unperturbed problem. In this paper, we consider the perturbed convex quadratic optimization problem and present a method to identify the support set expansi...

In this paper, we propose a churn management model based on a partial least square (PLS) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program. A PLS prediction model is first calibrated to estimate the churn probabilities of customers. Then this PLS prediction model is transformed into a control model af...

We study the minimization of a quadratic objective function in a distributed fashion. It is known that the min-sum algorithm can be applied to solve the minimization problem if the algorithm converges. We propose a min-summin message-passing algorithm which includes the min-sum algorithm as a special case. As the name suggests, the new algorithm involves two minimizations in each iteration as c...

We describe a new method for camera autocalibration and scaled Euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic parameters. The motion constancy of these is used to rectify an initial projective reconstruction. Euclidean scene structure is formulated in terms of the absolute quadric — the singular dual 3D quadric (4 4 rank 3 matr...

Ha 7] recently introduced the generalized proximal point algorithm for solving the generalized equation. In this note, we present the generalized proximal point algorithm for convex optimization problems based on Ha's work. The idea behind this algorithm is that instead of adding a quadratic term to all the variables, we add a quadratic term to a subset of the variables. We extend the criteria ...

In this paper the interval valued function is defined in the parametric form and its properties are studied. A methodology is developed to study the existence of the solution of a general interval optimization problem, which is expressed in terms of the interval valued functions. The methodology is applied to the interval valued convex quadratic programming problem.

In this paper we discuss image registration techniques with a focus on volume preserving constraints. These constraints can reduce the non-uniqueness of the registration problem significantly. Our implementation is based on a constrained optimization formulation. To solve the problem we use a variant of the Sequential Quadratic Programming method. Moreover, we present results on synthetic as we...

We consider the problem of finding a trellis for a finear block code that minimizes one or more measures of trellis complexity. The domain of optimization may be different permutations of the same code or different codes with the same parameters. Constraints on trellises, including relationships between the minimal trellis of a code and that of the dual code, are used to derive bounds on comple...

In recent years, binary quadratic programming (BQP) has been 7 successively applied to solve several combinatorial optimization problems. We 8 consider in this paper a study of using the BQP model to solve the minimum 9 sum coloring problem (MSCP). For this purpose, we recast the MSCP with 10 a quadratic model which is then solved via a recently proposed Path Relink11 ing (PR) algorithm designe...

In this paper we formulate a two-stage stochastic quadratic optimization problem, that models a production planning problem of multiple products, over several time periods, with uncertain demands. This model considers quadratic inventory holding and demand backlogging recourse costs, which model the saturation phenomenon inherent to the increase of the inventory and/or backlogged demand levels....