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This article provides instructions in preparing a paper for publication in the "Journal of Optimization Theory and Applications." Some of these guidelines are the following: 1)submission of manuscripts in triplicate; 2) reference to English as the official language of the journal; 3) inclusion of an abstract of at least 50 to 100 words in each contribution; and, 4) the abstract should be followed by a list of four to five key words identifying the subject.

Rufus P. Isaacs joined the RAND Corporation4. Santa Monica, California in 1948 and started to develop the theory of dynamic games in the early 1950s. Until winter 1954/55, when Isaacs left the RAND Corporation, he investigated two player. zero-sum dynamic games of the classic pursuit-evasion type. Prior to 1965, Isaacs published his theory only in internal RAND papers and research memoranda. In his first RAND paper (Ref. 1), Isaacs sketched the basic ideas of zero-sum dynamic game theory The ideas already included rudimentary precursors of the maximum principle, dynamic programming, and backward analysis. At the end of 1954 and the beginning of 1955. Isaacs summarized his research in four research memoranda (Refs. 3–6), which ten years later formed the basis of his famous book on Differential Games (Ref. 7). This paper survey's Isaacs' research with an emphasis on the early years of dynamic games. The readers are kindly invited to discuss the author's point of view. Comments and statements sent to the author will be summarized and published later. [ABSTRACT FROM AUTHOR]

This paper deals with undiscounted infinite stage two-person zero-sum stochastic games with finite state and action spaces. It was recently shown that such games possess a value. But in general there are no optimal strategies. We prove that for each player there exists a nonempty set of easy initial states, i.e. starting states for which the player possesses an optimal stationary strategy. This result is proved with the aid of facts derived by Bewley and Kohlberg for the limit discount equation for stochastic games. [ABSTRACT FROM AUTHOR]

This article provides the editorial policy of the "Journal of Optimization Theory and Applications." The journal publishes carefully selected papers covering mathematical optimization techniques and their applications to science and engineering. And it issues five types of contributions such as Survey Papers, Contributed Papers, and Technical Notes. Manuscripts, in triplicate, may be submitted to any of the Associate Editors or the Editor-in-Chief.

The present paper studies the following constrained vector optimization problem: $$\mathop {\min }\limits_C f(x),g(x) \in - K,h(x) = 0$$, where f: ℝ n → ℝ m, g: ℝ n → ℝ p are locally Lipschitz functions, h: ℝ n → ℝ q is C1 function, and C ⊂ ℝ m and K ⊂ ℝ p are closed convex cones. Two types of solutions are important for the consideration, namely w-minimizers (weakly efficient points) and i-minimizers (isolated minimizers of order 1). In terms of the Dini directional derivative first-order necessary conditions for a point x0 to be a w-minimizer and first-order sufficient conditions for x0 to be an i-minimizer are obtained. Their effectiveness is illustrated on an example. A comparison with some known results is done. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the multiple-objective optimization problems, considering an improved definition of generalized type I univex function. Some optimality conditions as well as some duality relations are established. [ABSTRACT FROM AUTHOR]

In this paper, properties of the entropic risk measure are examined rigorously in a general framework. This risk measure is then applied in a dynamic portfolio optimization problem, appearing in the risk management constraint. By considering the dual problem, we prove the existence and uniqueness of the solution and obtain an analytic expression for the solution. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a new robust FIR equalizer for discrete time systems subject to parameter uncertainties in the matrices of the system state-space model from an H8 perspective. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. The discrete algebraic Riccati inequality (DARI) and linear matrix inequality (LMI) provide an extremely superior methodology in the H8 control fields. By the H8 and robust methods in conjunction with the bounded real lemma, the robust FIR equalizer design is transformed into a standard LMI optimization problem. An example is given to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper considers the problem of finding minimal length tree networks on the unit sphere Φ of a given point set (V) where distance is measured along great circular arcs. The related problems of finding a Steiner Minimal Tree SMT (V) and of finding a Minimum Spanning Tree MST(V) are treated through a simplicial decomposition technique based on computing the Delaunay Triangulation DT (V) and the Voronoi Diagram VD(V) of the given point set. O (N log N) algorithms for computing DT(V), VD(V), and WST(V) as well as an O(N log N) heuristic for finding a sub-optimal SMT(V) solution are presented, together with experimental results for randomly distributed points on Φ. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to give a survey of methods, partly derived by the author in joint work with other researchers, concerning the problem of constructing e-optimal strategies for partially observable MDPs. The methods basically consist in transforming the problem into one of approximation: Starting from the original problem a sequence of approximating problems is constructed such that: (i) For each approximating problem an optimal strategy can actually be computed, (ii) Given ϵ » 0, there exists an approximating problem such that the optimal strategy for the latter is ϵ-optimal for the original problem. [ABSTRACT FROM AUTHOR]

Classification rules with a reserve judgment option provide a way to satisfy constraints on the misclassification probabilities when there is a high degree of overlap among the populations. Constructing rules which maximize the probability of correct classification while satisfying such constraints is a difficult optimization problem. This paper uses the form of the optimal solution to develop a relatively simple and computationally fast method for three populations which has a non parametric quality in controlling the misclassification probabilities. Simulations demonstrate that this procedure performs well. [ABSTRACT FROM AUTHOR]

In this paper, an efficient computation method is developed for solving a general class of minmax optimal control problems, where the minimum deviation from the violation of the continuous state inequality constraints is maximized. The constraint transcription method is used to construct a smooth approximate function for each of the continuous state inequality constraints. We then obtain an approximate optimal control problem with the integral of the summation of these smooth approximate functions as its cost function. A necessary condition and a sufficient condition are derived showing the relationship between the original problem and the smooth approximate problem. We then construct a violation function from the solution of the smooth approximate optimal control problem and the original continuous state inequality constraints in such a way that the optimal control of the minmax problem is equivalent to the largest root of the violation function, and hence can be solved by the bisection search method. The control parametrization and a time scaling transform are applied to these optimal control problems. We then consider two practical problems: the obstacle avoidance optimal control problem and the abort landing of an aircraft in a windshear downburst. [ABSTRACT FROM AUTHOR]

A temporal flexible planning problem that involves contingent and requirement events can be formulated as a simple temporal network with uncertainty (STNU). An STNU is controllable when there is a strategy for executing the requirement events (or actions) in such a way that all the conditions involving contingent events can be satisfied in all situations. The most interesting and useful controllability property is dynamic controllability in which the remaining actions in an STNU can always be scheduled under all possible feasible durations of future contingent events when all the past contingent events are known. In this paper, we propose and study a novel problem of assigning bounds on the duration of each requirement link in order for the resulting STNU to be dynamically controllable and to minimize the total cost over the allowed durations of all requirement links. We first prove the NP hardness of the problem with a linear cost function. We then formulate the dynamic controllability of an STNU as the constraints in a nonlinear optimization problem. Finally, we present methods for reducing the number of constraints in order to make the problem tractable and to demonstrate the computational performance of our methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the relationship between bilevel optimization and multicriteria optimization. Given a bilevel optimization problem, we introduce an order relation such that the optimal solutions of the bilevel problem are the nondominated points with respect to the order relation. In the case where the lower-level problem of the bilevel optimization problem is convex and continuously differentiable in the lower-level variables, this order relation is equivalent to a second, more tractable order relation. Then, we show how to construct a (nonconvex) cone for which we can prove that the nondominated points with respect to the order relation induced by the cone are also nondominated points with respect to any of the two order relations mentioned before. We comment also on the practical and computational implications of our approach. [ABSTRACT FROM AUTHOR]

In this paper, the robust asymptotic stability and stabilization for a class of uncertain neutral system with time-varying delays are considered. Based on the Lyapunov-Krasovskii functional theory, some stability and stabilization criteria are derived. Delay-dependent and delay-independent criteria are proposed for the stability and stabilization of the considered systems. State and output feedbacks are considered to stabilize the uncertain neutral system. A linear matrix inequality approach and a genetic algorithm are used to solve the stability and stabilization problems. Finally, some numerical examples are shown to illustrate the use of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the smoothing of an implicit function defined by a nonsmooth underdetermined system of equations F(y, z) =0. We apply a class of parametrized smoothing methods to smooth F and investigate the limiting behavior of the implicit function solving the smoothed equations. In particular, we discuss the approximation of the Clarke generalized Jacobian of the implicit function when F is piecewise smooth. As an application, we present an analysis of the generalized Karush-Kuhn-Tucker conditions of different forms for a piecewise-smooth equality-constrained minimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a simple multimembered evolution strategy to solve global nonlinear optimization problems. The approach does not require the use of a penalty function. Instead, it uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population. This technique helps the algorithm to find the global optimum despite reaching reasonably fast the feasible region of the search space. A simple feasibility-based comparison mechanism is used to guide the process toward the feasible region of the search space. Also, the initial stepsize of the evolution strategy is reduced in order to perform a finer search and a combined (discrete/intermediate) panmictic recombination technique improves its exploitation capabilities. The approach was tested with a well-known benchmark. The results obtained are very competitive when comparing the proposed approach against other state-of-the art techniques and its computational cost (measured by the number of fitness function evaluations) is lower than the cost required by the other techniques compared. [ABSTRACT FROM AUTHOR]

We outline a new approach for radiosurgery treatment planning, based on solving a series of optimization problems. We consider a specific treatment planning problem for a specialized device known as the gamma knife which provides an advanced stereotactic approach to the treatment, of tumors, vascular malformations, and pain disorders within the head. Tile sequence of optimization problems involves nonlinear and mixed integer programs whose solution is required in a given planning time (typically less than 30 minutes). This paper outlines several modeling decisions that result in more efficient and robust, solutions. Furthermore, it, outlines a new approach for determining starting points for the nonlinear programs, based on a skeletonization of the target volume. Treatment plans generated for real patient data show the efficiency of the approach. [ABSTRACT FROM AUTHOR]

We introduce a new barrier-type function which is not, a barrier function in the usual sense: it has finite value at the boundary of the feasible region. Despite this, the iteration bound of a large-update interior-point method based on this function is shown to be O(square root of n (log n) log n/epsilon), which is as good as tile currently best known bound for large update methods. The recently introduced properly of exponential convexity for the kernel function underlying the barrier function, as well as the strong convexity of the kernel function, are crucial in the analysis. [ABSTRACT FROM AUTHOR]

We derive a formula for the minimal initial wealth needed to hedge an arbitrary contingent claim in a continuous-time model with proportional transaction costs; the expression obtained can be interpreted as the supremum of expected discounted values of the claim, over all (pairs of) probability measures under which the "wealth process" is a supermartingale. Next, we prove the existence of an optimal solution to the portfolio optimization problem of maximizing utility from terminal wealth in the same model; we also characterize this solution via a transformation to a hedging problem: the optimal portfolio is the one that hedges the inverse of marginal utility evaluated at the shadow state-price density solving the corresponding dual problem, if such exists. We can then use the optimal shadow state-price density for pricing contingent claims in this market. The mathematical tools are those of continuous-time martingales, convex analysis, functional analysis, and duality theory. [ABSTRACT FROM AUTHOR]

In this paper, two different decision models for the planning of highway pavement improvements are presented. In the first model, we want to get a prescribed improvement in the state of the highway network with minimal agency cost. In the second model, a given amount of money is distributed between the highway sections in different states in such a way that the achieved improvements should be the best in some sense. The first model helps the administration in the estimation of the necessary cost for the annual highway improvements. The second model in addition gives an objective tool to the administration for the distribution of the total amount of money between the different regions of the country. We present the construction of the models in detail. Both of them use Markov transition probabilities according to the states of the highway sections and produce a special structure, large-scale, linear programming problem. Some numerical results are presented on the data from Hungarian highways. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm, based on a recently proposed method due to D.D. Morrison, for solving constrained optimization problems, and shows that it compares favorably with existing methods in several examples. [ABSTRACT FROM AUTHOR]

Two basic disadvantages of the symmetric rank one (SR1) update are that the SR1 update may not preserve positive definiteness when starting with a positive definite approximation and the SR1 update can be undefined. A simple remedy to these problems is to restart the update with the initial approximation, mostly the identity matrix, whenever these difficulties arise. However, numerical experience shows that restart with the identity matrix is not a good choice. Instead of using the identity matrix we used a positive multiple of the identity matrix. The used positive scaling factor is the optimal solution of the measure defined by the problem—maximize the determinant of the update subject to a bound of one on the largest eigenvalue. This measure is motivated by considering the volume of the symmetric difference of the two ellipsoids, which arise from the current and updated quadratic models in quasi-Newton methods. A replacement in the form of a positive multiple of the identity matrix is provided for the SR1 update when it is not positive definite or undefined. Our experiments indicate that with such simple initial scaling the possibility of an undefined update or the loss of positive definiteness for the SR1 method is avoided on all iterations. [ABSTRACT FROM AUTHOR]

Abstract: Optimization design is the most important key technique of Air-breathing Hypersonic Cruise Vehicle (HCV). To improve the design level and get better integrated performances of HCV, this paper researches the integrated optimization design method of waverider hypersonic cruise vehicle. In the optimization design, Multi-Objective Genetic Algorithms (MOGA) is selected as the optimization algorithm, and the shape parameters of aircraft are as design variables. Some performances, such as aerodynamics, aeroheating, radar cross section (RCS), airframe/scramjet integration, and volume of airframe, trimmed characteristic, static stability and maneuverability at cruise phase are selected as the objectives. When the optimization process finishes, the Pareto front side is got, in which many Pareto solutions whose integrated performances are more excellent than basic configuration are found. According to the design idea, we choose a Pareto solution from the Pareto front side as the recommended shape configuration for further research. To validate the aerodynamics of recommended configuration, wind tunnel test is done. And the comparison results show that the optimization design is successful. [Copyright &y& Elsevier]

Abstract: In this paper we study necessary and sufficient optimality conditions for a set-valued optimization problem. Convexity of the multifunction and the domain is not required. A definition of -approximating multifunction is introduced. This multifunction is the differentiability notion applied to the problem. A characterization of weak minimizers is obtained for invex and generalized -convexlike multifunctions using the Lagrange multiplier rule. [Copyright &y& Elsevier]

Abstract: Over the past few years there has been considerable progress in methods to systematically analyse the complexity of constraint satisfaction problems with specified constraint types. One very powerful theoretical development in this area links the complexity of a set of constraints to a corresponding set of algebraic operations, known as polymorphisms. In this paper we extend the analysis of complexity to the more general framework of combinatorial optimisation problems expressed using various forms of soft constraints. We launch a systematic investigation of the complexity of these problems by extending the notion of a polymorphism to a more general algebraic operation, which we call a multimorphism. We show that many tractable sets of soft constraints, both established and novel, can be characterised by the presence of particular multimorphisms. We also show that a simple set of NP-hard constraints has very restricted multimorphisms. Finally, we use the notion of multimorphism to give a complete classification of complexity for the Boolean case which extends several earlier classification results for particular special cases. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel method for the numerical solution of dynamic optimization problems. After obtaining a first solution at a coarse resolution of the control profiles with a direct sequential approach, the structure of the control profiles is analyzed for possible switching times and arcs. Subsequently, the problem is reformulated automatically and solved as a multi-stage problem, with each stage corresponding to a potential arc. Order and resolution of the control parameterization are adapted to the type of the particular arc. By means of some case studies we show that accurate solutions with only few degrees of freedom can be obtained. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new super-memory gradient method with curve search rule for unconstrained optimization problems. The method uses previous multi-step iterative information and some curve search rules to generate new iterative points at each iteration. This makes the new method converge stably and be more suitable for solving large scale optimization problems than other similar methods. We analyze the global convergence and convergence rate under some mild conditions. Numerical experiments show that some new algorithms are available and effective in practical computation. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to shaping of the frequency response of the sensitivity function. In this approach, a desired frequency response is assumed to be specified at a finite number of frequency points. A sensitivity shaping problem is formulated as an approximation problem to the desired frequency response with a function in a class of sensitivity functions with a degree bound. The sensitivity shaping problem is reduced to a finite-dimensional constrained nonlinear least-squares optimization problem. To solve the optimization problem numerically, standard algorithms for an unconstrained version of nonlinear least-squares problems are modified to incorporate the constraint. Numerical examples illustrate how these design parameters are tuned in an intuitive manner, as well as how the design proceeds in actual control problems. [Copyright &y& Elsevier]

This paper presents a unified framework for best-fitting of complex rigid surface to measured 3-D coordinate data by adjusting its location (position/orientation). For a point expressed in the machine reference frame and a nominal surface represented in its own model frame, a signed point-to-surface distance function is defined, and its properties are investigated, especially, its increment with respect to the differential motion of the surface, up to the second order, is derived. On this basis, localization and profile error evaluation of complex surface are formulated as a nonlinear least-squares problem and nonlinear constrained optimization problem respectively, and sequential approximation algorithms are developed to solve them. The two algorithms have the advantages of implementational simplicity, computational efficiency and robustness. Also strategies for estimating initial solution and compensating probe radius are presented. Examples confirm the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimality conditions of a class of special nonsmooth programming min F(χ)-...|max{f[subi](χ),c[subi]}| which arises from L[sub1]-norm optimization, where c[subi] ∈ R is constant and f[subi] ∈ C¹, i = 1,2,...,m. These conditions can easily be tested by computer. [ABSTRACT FROM AUTHOR]

Suppose an experiment requires the fitting of a linear relationship Y = α + βX to data (xi, yi) where X and Y were both measured subject to error. In this paper a variety of methods are used to estimate the value of the slope β. Many popular methods are reformulated into a single class of optimization problems. Each method studied seeks to minimize a combination of the sum of the weighted deviations in the horizontal and vertical directions. The reformulation of each method is presented along with the results of a simulation study of the methods. [ABSTRACT FROM AUTHOR]

The paper studies the problem of determining the measurements of a given number of sizes of apparel so as to maximize expected sales or minimize an index of discomfort. It develops general necessary conditions for optimization in one control body dimension and calculates optimal sizes for the special case where the population distribution is normal. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimizing a function over a finite or countably infinite set of alternatives, in situations where this objective function cannot be evaluated exactly, but has to be estimated or measured. A special focus is on situations where simulation is used to evaluate the objective function. We present two versions of a new iterative method for solving such discrete stochastic optimization problems. In each iteration of the proposed method, a neighbor of the "current" alternative is selected, and estimates of the objective function evaluated at the current and neighboring alternatives are compared. The alternative that has a better observed function value becomes the next current alternative. We show how one version of the proposed method can be used to solve discrete optimization problems where the objective function is evaluated using transient or steady-state simulation, and we show how the other version can be applied to solve a special class of discrete stochastic optimization problems and present some numerical results. A major strength of the proposed method is that it spends most of the computational effort at local minimizers of the objective function. In fact, we show that for both versions of the proposed method, the alternative that has been visited most often in the first m iterations converges almost surely to a local optimizer of the objective function as m goes to infinity. [ABSTRACT FROM AUTHOR]

We compare six state-of-the-art local optimization solvers, with a focus on there efficiency when invoked within an interval-based global optimization algorithm. For comparison purposes we design three special performance indicators: a solution check indicator (measuring whether the local minimizes found are good candidates for near-optimal verified feasible points), a function value indicator (measuring the contribution to the progress of the global search), and a running time indicator (estimating the computational cost of the local search within the global search). The solvers are compared on the COCONUT Environment test set consisting of 1307 problems. Our main goal is to predict the behavior of the solvers in terms of the three performances indicators on a new problem. For this we introduce a k-nearest neighbor method applied over a feature space consisting of several categorical and numerical features of the optimization problems. The quality and robustness of the prediction is demonstrated by various quality measurements with detailed comparative tests. In particular, we found that on the test set we are able to pick a "best" solver in 66 89% of the cases and avoid picking all "useless" solvers in 95-99% of the cases (when a useful alternative exists). The resulting automated solver selection method is implemented as an inference engine of the COCONUT Environment. [ABSTRACT FROM AUTHOR]

We discuss the complexity of a class of highly structured global optimization problems, namely the maximization of separable functions, with each one-dimensional component convex and nondecreasing, over polytopes defined by a 0-1 constraint matrix with at most two variables involved in each constraint. In particular, we prove some inapproximability and approximability results. [ABSTRACT FROM AUTHOR]

We consider single-carrier and single-user transmissions over a frequency-selective channel. We address the problem of joint estimation of the channel and the carrier frequency offset using a known training sequence (TS). Since in general the TS that is optimum for CFO estimation is not optimum for channel estimation, here we design the TS to minimize the mean-square error on the soft estimates of the data symbols obtained using a Wiener equalizer after CFO compensation. Our design results apply to the general scenarios of correlated and/or Ricean channel taps. Simulations measuring the bit error rate show an interesting gain when using the proposed training schemes in lieu of standard schemes. [ABSTRACT FROM AUTHOR]

Abstract  Several modifications that have been made to the NDDO core-core interaction term and to the method of parameter optimization are described. These changes have resulted in a more complete parameter optimization, called PM6, which has, in turn, allowed 70 elements to be parameterized. The average unsigned error (AUE) between calculated and reference heats of formation for 4,492 species was 8.0 kcal mol−1. For the subset of 1,373 compounds involving only the elements H, C, N, O, F, P, S, Cl, and Br, the PM6 AUE was 4.4 kcal mol−1. The equivalent AUE for other methods were: RM1: 5.0, B3LYP 6–31G*: 5.2, PM5: 5.7, PM3: 6.3, HF 6–31G*: 7.4, and AM1: 10.0 kcal mol−1. Several long-standing faults in AM1 and PM3 have been corrected and significant improvements have been made in the prediction of geometries. Figure Calculated structure of the complex ion [Ta6Cl12]2 (footnote): Reference value in parenthesis [ABSTRACT FROM AUTHOR]

We consider degenerate parabolic and elliptic fully nonlinear Bellman equations with Lipschitz coefficients in domains. Error bounds of order h1/2 in the sup norm for certain types of finite-difference schemes are obtained. [ABSTRACT FROM AUTHOR]

A robust structural optimization scheme as well as an optimization algorithm are presented based on the robustness function. Under the uncertainties of the external forces based on the info-gap model, the maximization of the robustness function is formulated as an optimization problem with infinitely many constraints. By using the quadratic embedding technique of uncertainty and the S-procedure, we reformulate the problem into a nonlinear semidefinite programming problem. A sequential semidefinite programming method is proposed which has a global convergent property. It is shown through numerical examples that optimum designs of various linear elastic structures can be found without difficulty. [ABSTRACT FROM AUTHOR]

We use the first and second order approximations of mappings to establish both necessary and sufficient optimality conditions for unconstrained and constrained nonsmooth vector optimization problems. Ideal solutions, efficient solutions, and weakly efficient solutions are considered. The data of the problems need not even be continuous. Some often imposed compactness assumptions are also relaxed. Examples are provided to compare our results and some known recent results. [ABSTRACT FROM AUTHOR]

We propose a new inexact line search rule and analyze the global convergence and convergence rate of related descent methods. The new line search rule is similar to the Armijo line-search rule and contains it as a special case. We can choose a larger stepsize in each line-search procedure and maintain the global convergence of related line-search methods. This idea can make us design new line-search methods in some wider sense. In some special cases, the new descent method can reduce to the Barzilai and Borewein method. Numerical results show that the new line-search methods are efficient for solving unconstrained optimization problems. [ABSTRACT FROM AUTHOR]

For an inequality constrained nonsmooth multiobjective optimization problem, where the objective and constraint functions are locally Lipschitz, a nonsmooth analogue of the Maeda-type Guignard constraint qualification is given; stronger Kuhn-Tucker type necessary optimality conditions are derived that are expressed in terms of upper convexificators. Moreover, other constraint qualifications sufficient for the nonsmooth analogue are introduced and their relationships are presented. [ABSTRACT FROM AUTHOR]

In this article, we propose a method to identify contamination events (location and time of release) by enhancing a mathematical method originally proposed by Carasso et al. (Carasso, A., Sanderson, J.G. and Hyman, J.M., 1978, Digital removal of random media image degradation by solving the diffusion equation backward in time. SIAM Journal of Numerical Analysis, 15(4)). The method of the Marching-Jury Backward Beam/Plate Equation, applied earlier to groundwater problems, is enhanced and coupled to discrete Fourier transform processing techniques to solve a two-dimensional (2D) advection-dispersion transport problem with homogeneous and isotropic parameters backwards in time. (Atmadja, J. and Bagtzoglou, A.C., 2001a, Pollution source identification in heterogeneous porous media. Water Resources Research, 37(8), 2113–2125; Bagtzoglou, A.C. and Atmadja, J., 2003, The marching-jury backward beam equation and quasi-reversibility methods for hydrologic inversion: application to contaminant plume spatial distribution recovery. Water Resources Research, 39(2), 1038. Cornacchiulo, D. and Bagtzoglou, A.C., 2002, The marching-jury backward plate equation for contaminant plume spatial distribution recovery in two-dimensional heterogeneous media: Computational Issues, In: S.M. Hassanizadeh, R.J. Schotting, W.G. Gray and G.F. Pinder (Eds.) Computational Methods for Subsurface Flow and Transport (Netherlands: Elsevier Publishers) pp. 461–468. The difficulties associated with this ill-posed, inverse problem are well-recognized (Atmadja, J. and Bagtzoglou, A.C., 2001b, State-of-the-art report on mathematical methods for groundwater pollution source identification. Environmental Forensics, 2(3), 205–214). We, therefore, enhance the method by integrating an optimization scheme that takes as input parameters the stabilization parameter, the transport velocities, and the coefficient of diffusion. The objective function is set as an equally weighted sum of different mass and peak errors that can be calculated based on a combination of exhaustive contaminant coverage at specific points in time (e.g., lidar) and/or point data collected at a continuously monitored network of chemical sensors or biosensors, which may be stationary or mobile. [ABSTRACT FROM AUTHOR]

We study the contractibility of the efficient solution set of strictly quasiconcave vector maximization problems on (possibly) noncompact feasible domains. it is proved that the efficient solution set is contractible if at least one of the objective functions is strongly quasiconcave and any intersection of level sets of the objective functions is a compact (possibly empty) set. This theorem generalizes the main result of Benoist (Ref. 1), which was established for problems on compact feasible domains. [ABSTRACT FROM AUTHOR]

The multidimensional assignment problem (MAPs) is a higher dimensional version of the standard linear assignment problem. Test problems of known solution are useful in exercising solution methods. A method of generating an axial MAP of controllable size with a known unique solution is presented. Certain characteristics of the generated MAPs that determine realism and difficulty are investigated. [ABSTRACT FROM AUTHOR]

In sensitivity analysis one wants to know how the problem and the optimal solutions change under the variation of the input data. We consider the case when variation happens in the right-hand side of the constraints and/or in the linear term of the objective function. We are interested to find the range of the parameter variation in Convex Quadratic Optimization (CQO) problems where the support set of a given primal optimal solution remains invariant. This question has been first raised in Linear Optimization (LO) and known as Type II (so-called Support Set Invariancy ) sensitivity analysis. We present computable auxiliary problems to identify the range of parameter variation in support set invariancy sensitivity analysis for CQO. It should be mentioned that all the given auxiliary problems are LO problems and can be solved by an interior point method in polynomial time. We also highlight the differences between the characteristics of support set invariancy sensitivity analysis for LO and CQO. [ABSTRACT FROM AUTHOR]

The problem of computing the effective transport and mechanical properties of disordered materials, modeled by random or correlated percolation networks, is studied as an optimization problem. We show that calculating an optimal distribution of the potentials (voltages, displacements, etc.) throughout a disordered material that minimizes its total energy reduces the computation times for calculating the effective properties by a factor that depending on the morphology of the system, ranges from 3 to 73. Hence, this offers significant speed-up over the most efficient numerical methods currently available. [ABSTRACT FROM AUTHOR]

Traveling Salesman, Steiner Tree, and many other famous geometric optimization problems are NP-hard. Since we do not expect to design efficient algorithms that solve these problems optimally, researchers have tried to design approximation algorithms, which can compute a provably near-optimal solution in polynomial time.We survey such algorithms, in particular a new technique developed over the past few years that allows us to design approximation schemes for many of these problems. For any fixed constant c> 0, the algorithm can compute a solution whose cost is at most (1 + c) times the optimum. (The running time is polynomial for every fixed c> 0, and in many cases is even nearly linear.) We describe how these schemes are designed, and survey the status of a large number of problems. [ABSTRACT FROM AUTHOR]