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A modified parallel variable distribution (PVD) algorithm for solving large-scale constrained optimization problems is developed, which modifies quadratic subproblem QP1 at each iteration instead of the QP01 of the SQP-type PVD algorithm proposed by C. A. Sagastiz'abal and M. V. Solodov in 2002.The algorithm can circumvent the difficulties associated with the possible inconsistency of QP01 subproblem of the original SQP method. Moreover, we introduce a nonmonotone technique instead of the penalty function to carry out the line search procedure with more flexibly. Under appropriate conditions, the global convergence of the method is established. In the final part, parallel numerical experiments are implemented on CUDA based on GPU (Graphics Processing unit). [ABSTRACT FROM AUTHOR]

Motivated by the method of Su and Pu (2009), we present an improved nonmonotone filter trust region algorithm for solving nonlinear equality constrained optimization. In our algorithmamodified nonmonotone filter technique is proposed and the restoration phase is not needed. At every iteration, in common with the composite-step SQP methods, the step is viewed as the sum of two distinct components, a quasinormal step and a tangential step. Amore relaxed accepted condition for trial step is given and a crucial criterion is weakened. Under some suitable conditions, the global convergence is established. In the end, numerical results show our method is effective. [ABSTRACT FROM AUTHOR]

In this paper, we propose an active set modified Polak-Ribiére-Polyak method for solving large-scale optimization with simple bounds on the variables. The active set is guessed by an identification technique at each iteration and the recently developed modified Polak-Ribiére-Polyak method is used to update the variables with indices outside of the active set. Under appropriate conditions, we show that the proposed method is globally convergent. Numerical experiments are presented using box constrained problems in the CUTEr test problem libraries. [ABSTRACT FROM AUTHOR]

Abstract: A 2D Unconstrained Third Order Shear Deformation Theory (UTSDT) is presented for the evaluation of tangential and normal stresses in moderately thick functionally graded cylindrical shells subjected to mechanical loadings. Eight types of graded materials are investigated. The functionally graded material consists of ceramic and metallic constituents. A four parameter power law function is used. The UTSDT allows the presence of a finite transverse shear stress at the top and bottom surfaces of the graded cylindrical shell. In addition, the initial curvature effect included in the formulation leads to the generalization of the present theory (GUTSDT). The Generalized Differential Quadrature (GDQ) method is used to discretize the derivatives in the governing equations, the external boundary conditions and the compatibility conditions. Transverse and normal stresses are also calculated by integrating the three dimensional equations of equilibrium in the thickness direction. In this way, the six components of the stress tensor at a point of the cylindrical shell or panel can be given. The initial curvature effect and the role of the power law functions are shown for a wide range of functionally cylindrical shells under various loading and boundary conditions. Finally, numerical examples of the available literature are worked out. [Copyright &y& Elsevier]

Abstract: In this paper a mathematical problem with linear flexible constraints is considered. In order to solve the problem an approach is proposed based on multiobjective linear programming. Indeed, allowing violations for the constraints, and using multiobjective linear programming to minimize these violations, a subset of solution set which has less violations, namely efficiently feasible set, is obtained. Then, the corresponding objective function is optimized over efficiently feasible set in order to obtain an optimal solution. An application of the proposed approach in pattern classification is introduced. [Copyright &y& Elsevier]

Abstract: In this paper we propose a simple meshless method of fundamental solutions (MFS) for detecting a sound-soft scatterer embedded in a host acoustic homogeneous medium from the measurement of the far-field pattern of the scattered wave for only one incoming direction. Further, when this measurement is contaminated with noise, we augment the MFS with a nonlinear constrained regularized minimization for obtaining a stable numerical solution of the inverse problem. Numerical results are presented and discussed. [Copyright &y& Elsevier]

Abstract: A higher order numerical discretization technique based on Minimum Sobolev Norm (MSN) interpolation was introduced in our previous work. In this article, the discretization technique is presented as a tool to solve two hard classes of PDEs, namely, the exterior Laplace problem and the biharmonic problem. The exterior Laplace problem is compactified and the resultant near singular PDE is solved using this technique. This finite difference type method is then used to discretize and solve biharmonic type PDEs. A simple book keeping trick of using Ghost points is used to obtain a perfectly constrained discrete system. Numerical results such as discretization error, condition number estimate, and solution error are presented. For both classes of PDEs, variable coefficient examples on complicated geometries and irregular grids are considered. The method is seen to have high order of convergence in all these cases through numerical evidence. Perhaps for the first time, such a systematic higher order procedure for irregular grids and variable coefficient cases is now available. Though not discussed in the paper, the idea seems to be easily generalizable to finite element type techniques as well. [Copyright &y& Elsevier]

In this study, to handle the design optimization of plate-fin heat exchangers, a constrained version of a newly introduced evolutionary algorithm, the imperialist competitive algorithm, is proposed. Imperialist competitive algorithm was initially established for unconstrained optimization problems and different strategies such as penalty functions were employed to handle the constraints in the previous studies. In the proposed constrained imperialist competitive algorithm, a feasibility-based ranking is employed in the imperialist competitive algorithm method. Seven design parameters and various constraints are considered for the design optimization in which minimum weight and minimum total annual cost were considered as the autonomous objective functions. The results are compared with those obtained by a genetic algorithm approach combined with a static penalty function scheme. Also, a comparison between the proposed algorithm and the original imperialist competitive algorithm is performed. The simulation results show that both the proposed algorithm and imperialist competitive algorithm are of higher accuracy than the genetic algorithm, which is commonly used. Also, the numerical experiment indicates that the modification in the imperialist competitive algorithm leads to better performance of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract: A derivative-free iterative method for solving bound-constrained underdetermined nonlinear systems is presented. The procedure consists of a quasi-Newton method that uses the Broyden update formula and a globalized line search that combines the strategy of Grippo, Lampariello and Lucidi with the Li and Fukushima one. Global convergence results are proved and numerical experiments are presented. [Copyright &y& Elsevier]

Abstract: In this paper, we extend the filled function method proposed in Lin et al. (2009) [10] to solve the constrained system of nonlinear equations, and we propose two methods to find a solution of such a system. Finally the efficiency of the present method is illustrated through some examples. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the Polak–Ribière (or Polak–Ribière plus) conjugate gradient method for solving optimality condition of an unconstrained minimization problem. We give two new steplength rules only using gradient, and under gradient-Lipschitz assumption prove this method’s global convergence correspondingly. Then, we develop a practical Polak–Ribière plus method whose steplength is located by one inequality only using gradient, and report promising numerical results on high accuracy solution for some standard test problems when compared to the state-of-art methods in this research direction. Importantly, our work provides a new idea of devising a practical version of the celebrated Polak–Ribière (or Polak–Ribière plus) method. [Copyright &y& Elsevier]

We give a set of test functions for H1∞, the algebra of bounded holomorphic functions on the disk with first derivative equal to 0, whose interpolation problem was studied by Davidson, Paulsen, Raghupathi and Singh (2009). We show that this set of test functions is minimal by relating these ideas to realization and interpolation problems. [ABSTRACT FROM AUTHOR]

Abstract: Conjugate gradient methods have been paid attention to, because they can be directly applied to large-scale unconstrained optimization problems. In order to incorporate second order information of the objective function into conjugate gradient methods, Dai and Liao (2001) proposed a conjugate gradient method based on the secant condition. However, their method does not necessarily generate a descent search direction. On the other hand, Hager and Zhang (2005) proposed another conjugate gradient method which always generates a descent search direction. In this paper, combining Dai–Liao’s idea and Hager–Zhang’s idea, we propose conjugate gradient methods based on secant conditions that generate descent search directions. In addition, we prove global convergence properties of the proposed methods. Finally, preliminary numerical results are given. [Copyright &y& Elsevier]

Abstract: In this paper, we consider numerical simulation to a class of constrained dynamic problems where the overall dynamics are determined by the interactions between two sub-systems. We present an iterative algorithm that naturally decouples the computation of the two sub-systems and that ensures an accurate and efficient solution procedure. We conduct rigorous error analysis for the convergence of the iterative algorithm, and verify the analytical results through careful numerical tests. [Copyright &y& Elsevier]

Abstract: A new trust-region algorithm for solving a constrained optimization problem is introduced. In this algorithm, an active set strategy is used together with multiplier method to convert the computation of the trial step to easy trust-region subproblem similar to this for the unconstrained case. A convergence theory for this algorithm is presented. Under reasonable assumptions, it is shown that the algorithm is globally convergent. In particular, it is shown that, in the limit, a subsequence of the iteration sequence satisfies one of four types of stationary conditions. Namely, the infeasible Mayer–Bliss conditions, Fritz John’s conditions, the infeasible Fritz John’s conditions or KKT conditions. Preliminary numerical experiment on the algorithm is presented. The performance of the algorithm is reported. The numerical results show that our approach is of value and merit further investigation. [Copyright &y& Elsevier]

Abstract: Simple modifications of the limited-memory BFGS method (L-BFGS) for large scale unconstrained optimization are considered, which consist in corrections (derived from the idea of conjugate directions) of the used difference vectors, utilizing information from the preceding iteration. For quadratic objective functions, the improvement of convergence is the best one in some sense and all stored difference vectors are conjugate for unit stepsizes. Global convergence of the algorithm is established for convex sufficiently smooth functions. Numerical experiments indicate that the new method often improves the L-BFGS method significantly. [Copyright &y& Elsevier]

Abstract: The generation of priority vectors from pairwise comparison matrices is an essential part of the Analytic Hierarchy Process. Perhaps the most popular approach for deriving the priority weights is the right eigenvalue method (REV) which was proposed by Saaty. Despite its popularity some shortcomings of the REV have been reported in literature. Among the alternative approaches one can find the statistical estimation techniques, methods founded on constrained optimization models and models based on fuzzy description of decision maker preferences. In this paper new optimization techniques for deriving priority weights are introduced. In the proposed approach, the constrained optimization models are based on the same idea which underlies the REV. The properties of the resulting prioritization techniques are studied via computer simulations. This study demonstrates that the new methods perform very well in comparison with other popular techniques known from literature. What is especially important, the new approach provides the decision maker with a meaningful index that can be used to measure consistency of his/her judgments. The new index is closely related to the well-known Saaty’s consistency index CI, but in difference to the latter, it can be applied to both reciprocal as well as nonreciprocal comparison matrices. Hence the new index can be considered as a natural extension of the CI to all types of matrices. Some additional advantages resulting from the new approach are discussed and illustrated by numerical examples. [Copyright &y& Elsevier]

We formulate and solve the problem of optimal channel coding and flow-rate control for serving deadline-constrained traffic with average delivery ratio requirements (typical of multimedia streaming and interactive real-time applications) over time-varying channels. To that end, we first characterize the largest set of arrival processes (rather than rates) whose deadline and delivery ratio requirements can be satisfied. Then, we propose a dynamic (channel) coding algorithm that provably satisfies the requirements of any arrival process in this region. This optimal dynamic algorithm evolves through simple iterations to utilize a combination of pricing and finite-horizon dynamic programming operations. Next, we proposed two low-complexity approximations of the algorithm that has provable performance. We also extend the setup to allow for a flow controller that adjusts the incoming flow rates to satisfy their delivery ratio constraints when the arrival process is unknown but controllable. We propose a joint dynamic coding and a rate control algorithm to solve this problem, and prove its stability under the stochastic system operation. We also apply these general results to an important wireless down-link broadcast scenario with and without random network coding capabilities. Our theoretical work is supported by extensive numerical studies, which also reveal that our dynamic coding strategy outperforms any static coding strategy by opportunistically exploiting the statistical variations in the arrival and channel processes. [ABSTRACT FROM PUBLISHER]

Abstract: This paper introduces the concept and characterization of Disturbance-Dwell-Time invariance (DDT-invariance) and Constraint Admissible DDT-invariance (CADDT-invariance) for constrained systems with additive disturbance under dwell-time switching. Using the characterization, necessary and sufficient conditions for DDT-invariance and algorithms for the computation of the minimal and maximal constraint admissible convex DDT-invariant sets are provided. Numerical examples of such sets for several systems are also provided. [Copyright &y& Elsevier]

Abstract: A new sequential quadratic programming (SQP) method for nonlinear inequality constrained optimization is proposed. The aim of this paper is to promote global convergence for SQP methods using a flexible step acceptance strategy which combines merit functions and filter techniques. Global convergence is proved under some reasonable assumptions and preliminary numerical results are reported. [Copyright &y& Elsevier]

Abstract: Nonlinear conjugate gradient (CG) methods have played an important role in solving large-scale unconstrained optimization. Their wide application in many fields is due to their low memory requirements and global convergence properties. Numerous studies and modifications have been conducted recently to improve this method. In this paper, a new class of conjugate gradient coefficients (βk ) that possess global convergence properties is presented. The global convergence result is established using exact line searches. Numerical result shows that the proposed formula is superior and more efficient when compared to other CG coefficients. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new penalty-free-type method for nonlinear equality constrained problems. The new algorithm uses trust region framework and feasibility safeguarding technique. Moreover, it has no choice of penalty parameter and penalty function as a merit function, and it does not use the filter technique to avoid the penalty function either. We analyze the global convergence of the main algorithm under the standard assumptions. The preliminary numerical tests are reported. [Copyright &y& Elsevier]

Abstract: A modified Artificial Bee Colony algorithm to solve constrained numerical optimization problems is presented in this paper. Four modifications related with the selection mechanism, the scout bee operator, and the equality and boundary constraints are made to the algorithm with the aim to modify its behavior in a constrained search space. Six performance measures found in the specialized literature are employed to analyze different capabilities in the proposed algorithm such as the ability and cost to generate feasible solutions, the capacity and cost to locate the feasible global optimum solution and the competency to improve feasible solutions. Three experiments, including a comparison with state-of-the-art algorithms, are considered in the test design where twenty four well-known benchmark problems with different features are utilized. The overall results show that the proposed algorithm differs in its behavior with respect to the original Artificial Bee Colony algorithm but its performance is improved, mostly in problems with small feasible regions due to the presence of equality constraints. [Copyright &y& Elsevier]

By adding one variable to the equality-or inequality-constrained minimization problems, a new simple penalty function is proposed. It is proved to be exact in the sense that under mild assumptions, the local minimizers of this penalty function are precisely the local minimizers of the original problem, when the penalty parameter is sufficiently large. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a suite of numerical methods contained within a Matlab toolbox for constructing complete particle-size distributions from diverse particle-size data. These centre around the application of a constrained cubic-spline interpolation to logit-transformed cumulative percentage frequency data. This approach allows for the robust prediction of frequency values for a set of common particle-size categories. The scheme also calculates realistic, smoothly tapering tails for open-ended distributions using a non-linear extrapolation algorithm. An inversion of established graphic measures to calculate graphic cumulative percentiles is also presented. The robustness of the interpolation–extrapolation model is assessed using particle-size data from 4885 sediment samples from The Netherlands. The influence of the number, size and position of particle-size categories on the accuracy of modeled particle-size distributions was investigated by running a series of simulations using the empirical data set. Goodness-of-fit statistics between modeled distributions and input data are calculated by measuring the Euclidean distance between log-ratio transformed particle-size distributions. Technique accuracy, estimated as the mean goodness-of-fit between repeat sample measurements, was used to identify optimum model parameters. Simulations demonstrate that the data can be accurately characterized by 22 equal-width particle-size categories and 63 equiprobable particle-size categories. Optimal interpolation parameters are highly dependent on the density and position of particle-size categories in the original data set and on the overall level of technique accuracy. [Copyright &y& Elsevier]

For solving large-scale unconstrained minimization problems, the nonlinear conjugate gradient method is welcome due to its simplicity, low storage, efficiency and nice convergence properties. Among all the methods in the framework, the conjugate gradient descent algorithm — CG_DESCENT is very popular, in which the generated directions descend automatically, and this nice property is independent of any line search used. In this paper, we generalize CG_DESCENT with two Barzilai–Borwein steplength reused cyclically. We show that the resulting algorithm owns attractive sufficient descent property and converges globally under some mild conditions. We test the proposed algorithm by using a large set of unconstrained problems with high dimensions in CUTEr library. The numerical comparisons with the state-of-the-art algorithm CG_DESCENT illustrate that the proposed method is effective, competitive, and promising. [ABSTRACT FROM AUTHOR]

The multiple-sets split feasibility problem (MSFP) captures various applications arising in many areas. Recently, by introducing a function measuring the proximity to the involved sets, Censor et al. proposed to solve the MSFP via minimizing the proximity function, and they developed a class of simultaneous methods to solve the resulting constrained optimization model numerically. In this paper, by combining the ideas of the proximity function and the operator splitting methods, we propose an efficient simultaneous method for solving the constrained MSFP. The efficiency of the new method is illustrated by some numerical experiments. [ABSTRACT FROM AUTHOR]

With enhancing mixing in micro- or nanofluidic applications in mind, the problem of maximizing fluid transport across a fluid interface subject to an available energy budget is examined. The optimum cross-interface perturbing velocity is obtained explicitly in the üme-periodic instance using an Euler-Lagrange constrained optimization approach. Numerical investigations which calculate transferred lobe areas and cross-interface flux are used to verify that the predicted strategy achieves optimum transport. Explicit active protocols for achieving this optimal transport are suggested. [ABSTRACT FROM AUTHOR]

Abstract: Hypothesis testing via numerical models has emerged as a powerful tool which permits the verification of theoretical frameworks against canonical experimental and clinical observations. Cleverly designed computational experiments also inspire new methodologies by elucidating important biological processes and restricting parametric spaces. Constrained mixture models of arterial growth and remodeling (G&R) can facilitate the design of computational experiments which can bypass technical limitations in the laboratory, by considering illustrative special cases. The resulting data may then inform the design of focused experimental techniques and the development of improved theories. This work is a survey of computational hypothesis-testing studies, which exploit the unique abilities offered by the constrained mixture theory of arterial G&R. Specifically, we explore the core hypotheses integrated in these models, review their basic mathematical conceptualizations, and recapitulate their most salient and illuminating findings. We then assess how a decade''s worth of constrained mixture models have contributed to a lucid, emerging picture of G&R mechanisms. [Copyright &y& Elsevier]

Abstract: Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to reduce the total number of function evaluations, local and global techniques may be combined. Moreover, the hybridization may provide a more effective trade-off between exploitation and exploration of the search space. In this study, we propose a new hybrid genetic algorithm based on a local pattern search that relies on an augmented Lagrangian function for constraint-handling. The local search strategy is used to improve the best approximation found by the genetic algorithm. Convergence to an -global minimizer is proved. Numerical results and comparisons with other stochastic algorithms using a set of benchmark constrained problems are provided. [Copyright &y& Elsevier]

This article addresses the L 2-gain analysis and control synthesis problem for a class of uncertain switched linear systems with saturating actuators and external disturbances. First, when the controllers are pre-given, an analysis condition on disturbance tolerance is established under which the state trajectory starting from the origin will remain inside a bounded set. By this condition, the problem of estimating disturbance tolerance capability is formulated as a constrained optimisation problem. Then, the restricted L 2-gain property is analysed over the set of tolerable disturbances. An upper bound on the restricted L 2-gain is estimated by solving a constrained optimisation problem. Furthermore, when controller gain matrices are design variables, these optimisation problems can be adapted for controller design. All the results are achieved by utilising the multiple Lyapunov functions method and presented in terms of an LMI optimisation-based approach. A numerical example is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We discuss the energy generation expansion planning with environmental constraints, formulated as a nonsmooth convex constrained optimization problem. To solve such problems, methods suitable for constrained nonsmooth optimization need to be employed. We describe a recently developed approach, which applies the usual unconstrained bundle techniques to a dynamically changing 'improvement function'. Numerical results for the generation expansion planning are reported. [ABSTRACT FROM AUTHOR]

Abstract: The solution of the augmented Lagrangian related system is a key ingredient of many iterative algorithms for the solution of saddle-point problems in constrained optimization with quasi-Newton methods. However, such problems are ill-conditioned when the penalty parameter tends to zero, whereas the error vanishes as . We present a new fast method based on a splitting penalty scheme to solve such problems with a judicious prediction–correction method. We prove that, due to the adapted right-hand side, the solution of the correction step only requires the approximation of operators independent of , when is taken sufficiently small. Hence, the proposed method is as cheaper as tends to zero. We apply the two-step scheme to efficiently solve the saddle-point problem with a penalty method. Indeed, that fully justifies the interest of the vector penalty-projection methods recently proposed by Angot et al. (2008) to solve the unsteady incompressible Navier–Stokes equations, for which we give the stability result and some quasi-optimal error estimates. Moreover, the numerical experiments confirm both the theoretical analysis and the efficiency of the proposed method which produces a fast splitting solution to augmented Lagrangian or penalty problems, possibly used as a suitable preconditioner to the fully coupled system. [Copyright &y& Elsevier]

Abstract: In this paper, a new line search filter algorithm for equality constrained optimization is presented. The approach belongs to the class of inexact Newton-like methods. It can also be regarded as an inexact version of generic sequential quadratic programming (SQP) methods. The trial step is obtained by truncatedly solving the primal–dual system based on any robust and efficient linear system solver. Practical termination tests for the linear system solver are established to ensure global convergence. Preliminary numerical results demonstrate the approach is potentially useful. [Copyright &y& Elsevier]

Abstract: Since the appearance of the Barzilai–Borwein (BB) step sizes strategy for unconstrained optimization problems, it received more and more attention of the researchers. It was applied in various fields of the nonlinear optimization problems and recently was also extended to optimization problems with bound constraints. In this paper, we further extend the BB step sizes to more general variational inequality (VI) problems, i.e., we adopt them in projection methods. Under the condition that the underlying mapping of the VI problem is strongly monotone and Lipschitz continuous and the modulus of strong monotonicity and the Lipschitz constant satisfy some further conditions, we establish the global convergence of the projection methods with BB step sizes. A series of numerical examples are presented, which demonstrate that the proposed methods are convergent under mild conditions, and are more efficient than some classical projection-like methods. [Copyright &y& Elsevier]

Abstract: In this fast communication, we derive the statistical resolution limit (SRL), characterizing the minimal parameter separation, to resolve two closely spaced known near-field sources impinging on a linear array. Toward this goal, we conduct on the first-order Taylor expansion of the observation model a Generalized Likelihood Ratio Test (GLRT) based on a Constrained Maximum Likelihood Estimator (CMLE) of the SRL. More precisely, the minimum separation between two near-field sources, that is detectable for a given probability of false alarm and a given probability of detection, is derived herein. Finally, numerical simulations are done to quantify the impact of the array geometry of the signal sources power distribution and of the array aperture on the statistical resolution limit. [Copyright &y& Elsevier]

Abstract: We investigate numerical algorithms for viscoelastic fluid flows with defective boundary conditions, where only flow rates or mean pressures are prescribed on parts of the boundary. The defective boundary condition problem is formulated as a minimization problem, where we seek boundary conditions of the flow equations which yield an optimal functional value. Two different approaches are considered in developing computational algorithms for the constrained optimization problem, and results of numerical experiments are presented to compare performance of the algorithms. [Copyright &y& Elsevier]

This paper introduces a new exact and smooth penalty function to tackle constrained min-max problems. By using this new penalty function and adding just one extra variable, a constrained min-max problem is transformed into an unconstrained optimization one. It is proved that, under certain reasonable assumptions and when the penalty parameter is sufficiently large, the minimizer of this unconstrained optimization problem is equivalent to the minimizer of the original constrained one. Numerical results demonstrate that this penalty function method is an effective and promising approach for solving constrained finite min-max problems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we give new results on the Alienor method of dimension reduction. This technique is used to solve multidimensional global optimization problems of type min x∈X f(x) where f is a non convex Lipschitz function and X a compact set of defined by Lipschitz constraints. The idea is to construct an α-dense curve h in the feasible set X. The global minimum of f on X is then approximated by the global minimum of f on the curve h. That is, our problem has become a one-dimensional problem which can be solved by the Piyavskii–Shubert method. Examples of these curves and numerical implementations on several test functions are given. [Copyright &y& Elsevier]

SUMMARY Optimal control problems with partial differential equations play an important role in many applications. The inclusion of bound constraints for the control poses a significant additional challenge for optimization methods. In this paper, we propose preconditioners for the saddle point problems that arise when a primal-dual active set method is used. We also show for this method that the same saddle point system can be derived when the method is considered as a semismooth Newton method. In addition, the projected gradient method can be employed to solve optimization problems with simple bounds, and we discuss the efficient solution of the linear systems in question. In the case when an acceleration technique is employed for the projected gradient method, this again yields a semismooth Newton method that is equivalent to the primal-dual active set method. We also consider the Moreau-Yosida regularization method for control constraints and efficient preconditioners for this technique. Numerical results illustrate the competitiveness of these approaches. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: We consider an efficient trust-region framework which employs a new nonmonotone line search technique for unconstrained optimization problems. Unlike the traditional nonmonotone trust-region method, our proposed algorithm avoids resolving the subproblem whenever a trial step is rejected. Instead, it performs a nonmonotone Armijo-type line search in direction of the rejected trial step to construct a new point. Theoretical analysis indicates that the new approach preserves the global convergence to the first-order critical points under classical assumptions. Moreover, superlinear and quadratic convergence are established under suitable conditions. Numerical experiments show the efficiency and effectiveness of the proposed approach for solving unconstrained optimization problems. [Copyright &y& Elsevier]

Abstract: A method based on constrained optimization for updating of an acoustic finite element model using pressure response is proposed in this paper. The constrained optimization problem is solved using sequential quadratic programming algorithm. Updating parameters related to the properties of the sound absorbers and the measurement errors are considered. Effectiveness of the method is demonstrated by numerical studies on a 2D rectangular cavity and a car cavity. It is shown that the constrained formulation, that includes lower and upper bounds on the updating parameters in the form of inequality constraints, is important for obtaining a correct updated model. It is seen that the proposed updating method is not only able to effectively update the model to obtain a close match between the finite element model pressure response and the reference pressure response, but is also able to identify the correction factors to the parameters in error with reasonable accuracy. [Copyright &y& Elsevier]

Considering that the global optimal solutions often locate on or near the boundary of the feasible region for many constrained optimization problems, a novel genetic algorithm was proposed in this paper. The basic idea of the proposed algorithm was to put feasible solutions and infeasible solutions into two different containers respectively. Subsequently, a new designed crossover operator (named inward-outward crossover operator) was used to a feasible solution and a infeasible solution, then a line search along a potential decent direction was used to improve the offspring so as to find a good solution on or near to the boundary of feasible region. By this search procedure, the possibility for obtaining the globally optimal solution is obviously enhanced, and similarly, the convergent speed is also strengthened. The "particle swarm mutation" inherited the advantages of Particle Swarm Optimization (PSO) algorithm and searched for the potential solution along the direction of the best current particle and the direction of the best individual of the whole swarm in the past. Selection operator retained a constant rate of infeasible solutions. Numerical results indicate that the proposed algorithm can be efficient to get global optimal solutions or near to them in smaller population and less iteration times. [ABSTRACT FROM AUTHOR]

When conducting remote mine-hunting operations with a sidescan-sonar-equipped vehicle, a lawn-mowing search pattern is standard if no prior information on potential target locations is available. Upon completion of this initial search, a list of contacts is obtained. The overall classification performance can be significantly improved by revisiting these contacts to collect additional looks. This paper provides, for the first time, a link between the recent literature which finds optimal secondary looks and optimal route planning software. Automated planning algorithms are needed to generate multiaspect routes to improve the performance of mine-hunting systems and increase the capability of navies to efficiently clear potential mine fields. This paper introduces two new numerical techniques designed to enable current remote mine-hunting systems to achieve secondary paths minimizing the total distance to be traveled and satisfying all motion and imaging constraints. The first “local” approach is based on a sequential algorithm dealing with more tractable subproblems, while the second is “global” and based on simulated annealing. These numerical techniques are applied to two test sites created for the Mongoose sea trial held at the 2007 Autonomous Underwater Vehicle (AUV) Fest, Panama City, FL. Highly satisfactory planning solutions are obtained. [ABSTRACT FROM PUBLISHER]

The paper presents a reformulation of the Karush-Kuhn- Tucker (KKT) system associated nonlinear programming problem into an equivalent system of smooth equations. Classical Newton method is applied to solve the system of equations. The superlinear convergence of the primal sequence, generated by proposed method, is proved. The preliminary numerical results with a problems test set are presented. [ABSTRACT FROM AUTHOR]

Abstract: The filled function method is considered as an efficient approach to solve the global optimization problems. In this paper, a new filled function method is proposed. Its main idea is as follows: a new continuously differentiable filled function with only one parameter is constructed for unconstrained global optimization when a minimizer of the objective function is found, then a minimizer of the filled function will be found in a lower basin of the objective function, thereafter, a better minimizer of the objective function will be found. The above process is repeated until the global optimal solution is found. The numerical experiments show the efficiency of the proposed filled function method. [Copyright &y& Elsevier]

Abstract: The league championship algorithm (LCA) is a new algorithm originally proposed for unconstrained optimization which tries to metaphorically model a League championship environment wherein artificial teams play in an artificial league for several weeks (iterations). Given the league schedule, a number of individuals, as sport teams, play in pairs and their game outcome is determined given known the playing strength (fitness value) along with the team formation (solution). Modelling an artificial match analysis, each team devises the required changes in its formation (a new solution) for the next week contest and the championship goes for a number of seasons. In this paper, we adapt LCA for constrained optimization. In particular: (1) a feasibility criterion to bias the search toward feasible regions is included besides the objective value criterion; (2) generation of multiple offspring is allowed to increase the probability of an individual to generate a better solution; (3) a diversity mechanism is adopted, which allows infeasible solutions with a promising objective value precede the feasible solutions. Performance of LCA is compared with comparator algorithms on benchmark problems where the experimental results indicate that LCA is a very competitive algorithm. Performance of LCA is also evaluated on well-studied mechanical design problems and results are compared with the results of 21 constrained optimization algorithms. Computational results signify that with a smaller number of evaluations, LCA ensures finding the true optimum of these problems. These results encourage that further developments and applications of LCA would be worth investigating in the future studies. [Copyright &y& Elsevier]

Abstract: This paper presents the design of walking motions of a biped robot by a computational intelligent method. The proposed method finds the parameters of the output functions that produce such motion in the closed-loop system which fulfill the requirements of stability and minimal energy control by formulating a constrained optimization problem. A set of parameters which produces periodic motions with low energy control magnitudes and its closed-loop system trajectories are shown through a numerical study case of a 3-DOF biped robot. [Copyright &y& Elsevier]

Abstract: The economic and economic statistical designs of an control chart comprise the constrained optimization problem, which involves the simultaneous use of continuous and discrete decision variables. The particle swarm optimization (PSO) technique is adapted to deal with both continuous and discrete variables as required by the optimization problem. A numerical example in the study of Rahim and Banerjee (1993) , which used the Gamma failure mechanism, is used in the current study to indicate the procedure for solving the PSO algorithm performance. The results are compared with those from Genetic Algorithm, a popular evolutionary technique in the field of control charts under the same conditions. PSO is found to be a promising method for solving the problems of inherent in the economic and economic statistical designs of an control chart. [Copyright &y& Elsevier]

Cell formation (CF) is the most well-known problem in designing cellular manufacturing systems. In the generalised version of the CF problem, each part has a number of alternative process plans. Two issues should be addressed in the generalised cell formation (GCF) problem: selecting the best process plan for each part and assigning machines to the cells. In addition to the minimisation of movement costs, the selection of the most reliable process plans is an important task for the GCF problem. Although the expected value (EV) method is a straightforward procedure to model stochastic situations, this method has some serious drawbacks. Therefore, we use chance-constrained programming (CCP) to model this problem. To demonstrate the advantages of the proposed model, the model is compared with the traditional GCF model and the EV model through some numerical examples. The results reveal that the proposed model is superior to the other alternatives. [ABSTRACT FROM AUTHOR]