A New Approach to Optimization Under Monotonic Constraint

A new efficient branch and bound method is proposed for solving convex programs with an additional monotonic nonconvex constraint. Computational experiments demonstrated that this method is quite practical for solving rank k reverse convex programs with much higher values of k than previously considered in the literature and can be applied to a wider class of nonconvex problems.     

Unified Gradient Approach to Performance Optimization Under a Pole Assignment Constraint

General closed-loop performance optimization problems with pole assignment constraint are considered in this paper under a unified framework. By introducing a free-parameter matrix and a matrix function based on the solution of a Sylvester equation, the constrained optimization problem is transformed into an unconstrained one, thus reducing the problem of closed-loop performance optimization with pole placement constraint to the computation of the gradient of the performance index with respect to the free-parameter matrix. Several classical performance indices are then optimized under the pole placement constraint. The effectiveness of the proposed gradient method is illustrated with an example.     

An Iterative Approach to Quadratic Optimization

Assume that C ₁, . . . , C _N are N closed convex subsets of a real Hilbert space H having a nonempty intersection C. Assume also that each C _i is the fixed point set of a nonexpansive mapping T _i of H. We devise an iterative algorithm which generates a sequence (x _n ) from an arbitrary initial x ₀H. The sequence (x_n) is shown to converge in norm to the unique solution of the quadratic minimization problem min _xC (1/2)Ax, x–x, u, where A is a bounded linear strongly positive operator on H and u is a given point in H. Quadratic–quadratic minimization problems are also discussed.     

ADAPT: An Adaptive Approach to Media Decisions

A firm's market changes under the impact of the firm's advertising. Feedback information on the market response allows the firm to learn about such changes and to adapt its subsequent media decisions accordingly. This paper presents an adaptive media model (ADAPT) that utilizes feedback information to revise certain parameters in a media decision model. Simulation experiments conducted with the ADAPT model demonstrate how different types of feedback information affect the media decisions and thereby the expected net profit contributions from sales. It is shown how the economic value of feedback information depends on the characteristics of the firm's market and also on the revision rules applied for updating the parameters. The value of the information may actually be negative if it is not used "intelligently".     

An Efficient Sampling Approach to Multiobjective Optimization

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savings of this new algorithm versus the traditional constraint method increase dramatically when the number of objectives increases. A real world case study of multiobjective optimal design of a best available control technology for Nitrogen Oxides (NOx) and Sulfur Oxides (SOx) reduction illustrates the usefulness of this approach.     

Formulation and Execution of Structural Topology Optimization for Practical Design Solutions

Topology optimization has gained prime significance due to increasing demands of lightweight components. In this paper, a general mathematical formulation of topology optimization is presented with some imperative manufacturing constraints for maximizing the stiffness of a structure with mixed boundary conditions. A methodology is implemented to determine the optimal configuration of operative structural components by executing TOSCA in batch-process mode with ANSYS software. CAD viable design is attained by smoothing the topological optimized surfaces. The geometry at the maximum stressed areas is also optimized. Analysis of the customized reduced weight configuration reveals that it comprises the harmonized stress distribution and improved structural performance.     

An Approach to Shape Optimization with State Constraints

We introduce a new approach to solve shape optimization problems with state constraints. The problem is reformulated on a fixed reference domain using conformal pull-back. The shape dependence is then hidden in the conformal parameter. The problem is discretized using FEM and solved as an NLP. Finally the optimal shape can be reconstructed from the optimal conformal parameter. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Solving Fixed-Charge Network Flow Problems with a Hybrid Optimization and Constraint Programming Approach

We apply to fixed charge network flow (FCNF) problems a general hybrid solution method that combines constraint programming and linear programming. FCNF problems test the hybrid approach on problems that are already rather well suited for a classical 0–1 model. They are solved by means of a global constraint that generates specialized constraint propagation algorithms and a projected relaxation that can be rapidly solved as a minimum cost network flow problem. The hybrid approach ran about twice as fast as a commercial mixed integer programming code on fixed charge transportation problems with its advantage increasing with problem size. For general fixed charge transshipment problems, however, it has no effect because the implemented propagation methods are weak.     

How to Speed up Optimization Algorithm in Topology Optimization of Continuum Structure

The problem of obtaining the optimal topology within very small step number during the optimization process is considered. The optimization procedure cuts the density in these design points where the energy decreases and adds the mass to these design points where the energy increases. To improve and speed up the process of moving mass the border functions are introduced.     

An Optimization Problem with a Separable Non-Convex Objective Function and a Linear Constraint

For a class of global optimization (maximization) problems, with a separable non-concave objective function and a linear constraint a computationally efficient heuristic has been developed.The concave relaxation of a global optimization problem is introduced. An algorithm for solving this problem to optimality is presented. The optimal solution of the relaxation problem is shown to provide an upper bound for the optimal value of the objective function of the original global optimization problem. An easily checked sufficient optimality condition is formulated under which the optimal solution of concave relaxation problem is optimal for the corresponding non-concave problem. An heuristic algorithm for solving the considered global optimization problem is developed.The considered global optimization problem models a wide class of optimal distribution of a unidimensional resource over subsystems to provide maximum total output in a multicomponent systems.In the presented computational experiments the developed heuristic algorithm generated solutions, which either met optimality conditions or had objective function values with a negligible deviation from optimality (less than 1/10 of a percent over entire range of problems tested).     

An Approach to the Optimization and Approximation of Solutions of any Operator Inclusion

In this paper we shall present the optimization problem in a set of solutions of operator inclusion with the maximal monotone operator. Then we treat optimization problem by Galerkin method and we prove convergence of optimal values of approximated optimization problems to the one for the original problem. Finally, we apply the results to give a simple example.     

Algebraic Approach to Duality in Optimization and Applications

Set-Valued and Variational Analysis - This paper studies duality of optimization problems in a vector space without topological structure. A strong duality relation is established by means of...     

A Pseudo-Global Optimization Approach with Application to the Design of Containerships

In this paper, we present a new class of pseudo-global optimization procedures for solving formidable optimization problems in which the objective and/or constraints might be analytically complex and expensive to evaluate, or available only as black-box functions. The proposed approach employs a sequence of polynomial programming approximations that are constructed using the Response Surface Methodology (RSM), and embeds these within a branch-and-bound framework in concert with a suitable global optimization technique. The lower bounds constructed in this process might only be heuristic in nature, and hence, this is called a pseudo-global optimization approach. We develop two such procedures, each employing two alternative branching techniques, and apply these methods to the problem of designing containerships. The model involves five design variables given by the design draft, the depth at side, the speed, the overall length, and the maximum beam. The constraints imposed enforce the balance between the weight and the displacement, a required acceptable length to depth ratio, a restriction on the metacentric height to ensure that the design satisfies the Coast Guard wind heel criterion, a minimum freeboard level as governed by the code of federal regulations (46 CFR 42), and a lower bound on the rolling period to ensure sea-worthiness. The objective function seeks to minimize the required freight rate that is induced by the design in order to recover capital and operating costs, expressed in dollars per metric ton per nautical mile. The model formulation also accommodates various practical issues in improving the representation of the foregoing considerations, and turns out to be highly nonlinear and nonconvex. A practical test case is solved using the proposed methodology, and the results obtained are compared with those derived using a contemporary commercialized design optimization tool. The prescribed solution yields an improved design that translates to an estimated increase in profits of about $18.45 million, and an estimated 27% increase in the return on investment, over the life of the ship.     

Global Optimization Approach to Unequal Global Optimization Approach to Unequal Sphere Packing Problems in 3D

The problem of the unequal sphere packing in a 3-dimen-sional polytope is analyzed. Given a set of unequal spheres and a poly-tope, the double goal is to assemble the spheres in such a way that (i) they do not overlap with each other and (ii) the sum of the volumes of the spheres packed in the polytope is maximized. This optimization has an application in automated radiosurgical treatment planning and can be formulated as a nonconvex optimization problem with quadratic constraints and a linear objective function. On the basis of the special structures associated with this problem, we propose a variety of algorithms which improve markedly the existing simplicial branch-and-bound algorithm for the general nonconvex quadratic program. Further, heuristic algorithms are incorporated to strengthen the efficiency of the algorithm. The computational study demonstrates that the proposed algorithm can obtain successfully the optimization up to a limiting size.     

Numerical Bounds for Semi-Markovian Quantities and Application to Reliability

We propose new easily computable bounds for different quantities which are solutions of Markov renewal equations linked to some continuous-time semi-Markov process (SMP). The idea is to construct two new discrete-time SMP which bound the initial SMP in some sense. The solution of a Markov renewal equation linked to the initial SMP is then shown to be bounded by solutions of Markov renewal equations linked to the two discrete time SMP. Also, the bounds are proved to converge. To illustrate the results, numerical bounds are provided for two quantities from the reliability field: mean sojourn times and probability transitions.      

Error Bounds and Implicit Multifunction Theorem in Smooth Banach Spaces and Applications to Optimization

In this paper we provide an error bound estimate and an implicit multifunction theorem in terms of smooth subdifferentials and abstract subdifferentials. Then, we derive a subdifferential calculus and Fritz–John type necessary optimality conditions for constrained minimization problems.