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Though multiresolution analysis has found successful applications on Acoustic emission (AE) signals, existing wavelets suffer from the problem of expressing AE signals in many applications. In the field of rail defect detection, complicated AE signals emitted from rail crack growth cannot be described properly by commonly used wavelets. In order to give better expressions of these signals, this paper proposes a design approach for signal-adapted wavelet in the frame of a two-band analysis/synthesis system, in which the problem of wavelet design is converted into a constrained optimization problem. For perfect reconstruction of the system, necessary and sufficient conditions, as well as orthogonality and normalization of the wavelet, are considered as constraints of the optimization problem. In order to preserve more information of crack growth AE signals, Euclid norm of the error between the approximation signal and the input signal is minimized in the cost function. AE signals of crack growths from a tensile test of a steel specimen are acquired for experimental verification. By comparing with the commonly used wavelets, it is demonstrated that the wavelet designed by the proposed method has superior performance in expressing the crack AE signal, and can outperform the most suitable existing wavelet due to its adaptivity. Moreover, the designed wavelet shows good robustness against noise, which has profound meaning for rail crack detection in practical applications. [ABSTRACT FROM AUTHOR]

A new subspace minimization conjugate gradient algorithm with a nonmonotone Wolfe line search is proposed and analyzed. In the scheme, we propose two choices of the search direction by minimizing a quadratic approximation of the objective function in special subspaces, and state criterions on how to choose the direction. Under given conditions, we obtain the significant conclusion that each choice of the direction satisfies the sufficient descent property. Based on the idea on how the function is close to a quadratic function, a new strategy for choosing the initial stepsize is presented for the line search. With the used nonmonotone Wolfe line search, we prove the global convergence of the proposed method for general nonlinear functions under mild assumptions. Numerical comparisons are given with well-known CGOPT and CG_DESCENT and show that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

Approximation is central to many optimization problems and the supporting theory provides insight as well as foundation for algorithms. In this paper, we lay out a broad framework for quantifying approximations by viewing finite- and infinite-dimensional constrained minimization problems as instances of extended real-valued lower semicontinuous functions defined on a general metric space. Since the Attouch-Wets distance between such functions quantifies epi-convergence, we are able to obtain estimates of optimal solutions and optimal values through bounds of that distance. In particular, we show that near-optimal and near-feasible solutions are effectively Lipschitz continuous with modulus one in this distance. Under additional assumptions on the underlying metric space, we construct approximating functions involving only a finite number of parameters that still are close to an arbitrary extended real-valued lower semicontinuous functions. [ABSTRACT FROM AUTHOR]

Sequential optimality conditions for constrained optimization are necessarily satisfied by local minimizers, independently of the fulfillment of constraint qualifications. These conditions support the employment of different stopping criteria for practical optimization algorithms. On the other hand, when an appropriate property on the constraints holds at a point that satisfies a sequential optimality condition, such a point also satisfies the Karush-Kuhn-Tucker conditions. Those properties will be called strict constraint qualifications in this paper. As a consequence, for each sequential optimality condition, it is natural to ask for its weakest strict associated constraint qualification. This problem has been solved in a recent paper for the Approximate Karush-Kuhn-Tucker sequential optimality condition. In the present paper, we characterize the weakest strict constraint qualifications associated with other sequential optimality conditions that are useful for defining stopping criteria of algorithms. In addition, we prove all the implications between the new strict constraint qualifications and other (classical or strict) constraint qualifications. [ABSTRACT FROM AUTHOR]

We propose a method for equality-constrained optimization based on a problem in which all constraints are systematically regularized. The regularization is equivalent to applying an augmented Lagrangian method but the linear system used to compute a search direction is reminiscent of regularized sequential quadratic programming. A limited-memory BFGS approximation to second derivatives allows us to employ iterative methods for linear least squares to compute steps, resulting in a factorization-free implementation. We establish global and fast local convergence under weak assumptions. In particular, we do not require the LICQ and our method is suitable for degenerate problems. Preliminary numerical experiments show that a factorization-based implementation of our method exhibits significant robustness while a factorization-free implementation, though not as robust, is promising. We briey discuss generalizing our framework to other classes of methods and to problems with inequality constraints. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient (semi-closed-form) zero-forcing (ZF) precoder design for the weighted sum-rate maximization problem under per-antenna power constraint (PAPC). Existing approaches for this problem are based on either interior-point methods that do not favorably scale with the problem size or subgradient methods that are widely known to converge slowly. To address these shortcomings, our proposed method is derived from three elements: minimax duality, alternating optimization (AO), and successive convex approximation (SCA). Specifically, the minimax duality is invoked to transform the considered problem into an equivalent minimax problem, for which we then recruit AO and SCA to find a saddle point, which enables us to take advantages of closed-form expressions and hence achieve fast convergence rate. Moreover, the complexity of the proposed method scales linearly with the number of users, compared to cubically for the standard interior-point methods. We provide an analytical proof for the convergence of the proposed method and numerical results to demonstrate its superior performance over existing approaches. Our proposed method offers a powerful tool to characterize the achievable rate region of ZF schemes under PAPC for massive multiple-input multiple-output. [ABSTRACT FROM PUBLISHER]

Recently, a form of extremum seeking for control (ESC) was developed for the stabilization of uncertain nonlinear systems. In ESC, the extremum seeker itself directly controls the systems through feedback rather than fine tuning a controller. The ESC results, and other related results, apply only to systems affine in control. However, in most physical systems, the control effort enters the system's dynamics through a nonlinear function, such as an input with deadzone and saturation. In this work, we use our previous results on ESC to develop constrained extremum seeking stabilizing controllers for systems of practical interest that are nonaffine in control. Considering that any odd function can be approximated arbitrarily well by an odd polynomial, we present analytic results for designing controllers for systems in which control enters the system dynamics through an odd polynomial. Furthermore, we study the robustness of the scheme to uncertainty in the odd polynomial degree as well as robustness to even-powered perturbations. [ABSTRACT FROM AUTHOR]

In this paper, a general methodology to approximate sets of data points through Non-uniform Rational Basis Spline (NURBS) curves is provided. The proposed approach aims at integrating and optimizing the full set of design variables (both integer and continuous) defining the shape of the NURBS curve. To this purpose, a new formulation of the curve fitting problem is required: it is stated in the form of a constrained nonlinear programming problem by introducing a suitable constraint on the curvature of the curve. In addition, the resulting optimization problem is defined over a domain having variable dimension, wherein both the number and the value of the design variables are optimized. To deal with this class of constrained nonlinear programming problems, a global optimization hybrid tool has been employed. The optimization procedure is split in two steps: firstly, an improved genetic algorithm optimizes both the value and the number of design variables by means of a two-level Darwinian strategy allowing the simultaneous evolution of individuals and species; secondly, the optimum solution provided by the genetic algorithm constitutes the initial guess for the subsequent gradient-based optimization, which aims at improving the accuracy of the fitting curve. The effectiveness of the proposed methodology is proven through some mathematical benchmarks as well as a real-world engineering problem. [ABSTRACT FROM AUTHOR]

Distributionally robust optimization (DRO) has become a popular research topic since it can solve stochastic programs with ambiguous distribution information. In this paper, as the background of economic dispatch (ED) in renewable integration systems, we present a new DRO-based ED optimization framework (DRED). The new DRED is addressed with a coupled format of distribution uncertainty for objective and chance constraints, which is different from most existing DRO frameworks. Some approximation strategies are adopted to handle the complicated DRED: the data-driven approach, the approximation of chance constraints by conditional value-at-risk, and the discrete scheme. The approximate reformulations are solvable nonconvex nonlinear programming problems. The approximation error analysis and convergence analysis are also established. Numerical results using an IEEE-30 buses system are presented to demonstrate the approach proposed in this paper. [ABSTRACT FROM AUTHOR]

We combine multiple sets of variable-precision full-field simulations within a single surrogate model. The approach is based on an original formulation of the 'Constrained Proper Orthogonal Decomposition' (C-POD), interpolating precise, albeit costly, high-fidelity data and approximating abondant, yet less accurate, lower-fidelity data. We compute the optimal high-dimensional subspace spanning the sparse high-fidelity full-field solutions and refine the output subspace definition thanks to the orthogonal information contained in abondant low-fidelity full-field solutions. We then build 'hierarchised' multi-fidelity surrogate models based on the previously refined subspace and giving a fast estimation of the high-fidelity full-field solution of any new location in the design space. The proposed model is illustrated by exploring the prediction of an analytical 2 D functional space on the one hand, and demonstrated by studying the efficiency of a 1.5-stage low-pressure compressor on the other. [ABSTRACT FROM AUTHOR]

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. In this paper, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robust optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. We prove consistency of our approximated min-max problem and conclude with numerical results. [ABSTRACT FROM AUTHOR]

Nonlinear chance constrained optimization (CCOPT) problems are known to be difficult to solve. This work proposes a smooth approximation approach consisting of an inner and an outer analytic approximation of chance constraints. In this way, CCOPT is approximated by two parametric nonlinear programming (NLP) problems which can be readily solved by an NLP solver. Any optimal solution of the inner approximation problem is a priori feasible to the CCOPT. The solutions of the inner and outer problems, respectively, converge asymptotically to the optimal solution of the CCOPT. [ABSTRACT FROM AUTHOR]

We devise and analyze a new stabilized finite element method to solve the first-order transport (or advection–reaction) equation. The method combines the usual Galerkin/Least-Squares approach to achieve stability with a nonlinear consistent penalty term inspired by recent discretizations of contact problems to weakly enforce a positivity condition on the discrete solution. We prove the existence and the uniqueness of the discrete solution. Then we establish quasi-optimal error estimates for smooth solutions bounding the usual error terms in the Galerkin/Least-Squares error analysis together with the violation of the maximum principle by the discrete solution. Numerical examples are presented to illustrate the performances of the method. [ABSTRACT FROM AUTHOR]

We present a competitive algorithm to minimize a locally Lipschitz function constrained with locally Lipschitz constraints. The approach is to use an ℓ nonsmooth penalty function. The method generates second order descent directions to minimize the ℓ penalty function. We introduce a new criterion to decide upon acceptability of a Goldstein subdifferential approximation. We show that the new criterion leads to an improvement of the Goldstein subdifferential approximation, as introduced by Mahdavi-Amiri and Yousefpour. Also, making use of our proposed line search strategy, the method always moves on differentiable points. Furthermore, the method has an adaptive behaviour in the sense that, when the iterates move on adequately smooth regions, the search directions switch exactly to the Shanno's conjugate gradient directions and no subdifferential approximation is computed. The global convergence of the algorithm is established. Finally, an extensive comparison of the results obtained by our proposed algorithm with the ones obtained by some well-known methods shows significant reductions in number of function and gradient evaluations. [ABSTRACT FROM AUTHOR]

In this paper we introduce, analyze, and compare several approaches designed to incorporate a linear (or affine) constraint within the Proper Generalized Decomposition framework. We apply the considered methods and numerical strategies to two classes of problems: the pure Neumann case where the role of the constraint is to recover unicity of the solution; and the Robin case, where the constraint forces the solution to move away from the already existing unique global minimizer of the energy functional. [ABSTRACT FROM AUTHOR]

We propose a method for constrained and unconstrained nonlinear multiobjective optimization problems that is based on an SQP-type approach. The proposed algorithm maintains a list of nondominated points that is improved both for spread along the Pareto front and optimality by solving single-objective constrained optimization problems. These single-objective problems are derived as SQP problems based on the given nondominated points. Under appropriate differentiability assumptions we discuss convergence to local optimal Pareto points. We provide numerical results for a set of unconstrained and constrained multiobjective optimization problems in the form of performance and data profiles, where several performance metrics are used. The numerical results confirm the superiority of the proposed algorithm against a state-of-the-art multiobjective solver and a classical scalarization approach, both in the quality of the approximated Pareto front and in the computational effort necessary to compute the approximation. [ABSTRACT FROM AUTHOR]

By means of Wolfe conditions strategy, we propose a quasi-Newton trust-region method to solve box constrained optimization problems. This method is an adequate combination of the compact limited memory BFGS and the trust-region direction while the generated point satisfies the Wolfe conditions and therefore maintains a positive-definite approximation to the Hessian of the objective function. The global convergence and the quadratic convergence of this method are established under suitable conditions. Finally, we compare our algorithms (IWTRAL and IBWTRAL) with an active set trust-region algorithm (ASTRAL) Xu and Burke (2007) on the CUTEst box constrained test problems presented by Gould et al. (2015). Numerical results show that the presented method is competitive and totally interesting for solving box constrained optimization. [ABSTRACT FROM AUTHOR]

We propose a novel actor–critic algorithm with guaranteed convergence to an optimal policy for a discounted reward Markov decision process. The actor incorporates a descent direction that is motivated by the solution of a certain non-linear optimization problem. We also discuss an extension to incorporate function approximation and demonstrate the practicality of our algorithms on a network routing application. [ABSTRACT FROM AUTHOR]

A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at . [ABSTRACT FROM AUTHOR]

We present new results for the Frank-Wolfe method (also known as the conditional gradient method). We derive computational guarantees for arbitrary step-size sequences, which are then applied to various step-size rules, including simple averaging and constant step-sizes. We also develop step-size rules and computational guarantees that depend naturally on the warm-start quality of the initial (and subsequent) iterates. Our results include computational guarantees for both duality/bound gaps and the so-called FW gaps. Lastly, we present complexity bounds in the presence of approximate computation of gradients and/or linear optimization subproblem solutions. [ABSTRACT FROM AUTHOR]

In rigorous constrained global optimization, upper bounds on the objective function help to reduce the search space. Obtaining a rigorous upper bound on the objective requires finding a narrow box around an approximately feasible solution, which then must be verified to contain a feasible point. Approximations are easily found by local optimization, but the verification often fails. In this paper we show that even when the verification of an approximate feasible point fails, the information extracted from the results of the local optimization can still be used in many cases to reduce the search space. This is done by a rigorous filtering technique called constraint aggregation. It forms an aggregated redundant constraint, based on approximate Lagrange multipliers or on a vector valued measure of constraint violation. Using the optimality conditions, two-sided linear relaxations, the Gauss-Jordan algorithm and a directed modified Cholesky factorization, the information in the redundant constraint is turned into powerful bounds on the feasible set. Constraint aggregation is especially useful since it also works in a tiny neighborhood of the global optimizer, thereby reducing the cluster effect. A simple introductory example demonstrates how our new method works. Extensive tests show the performance on a large benchmark. [ABSTRACT FROM AUTHOR]

This paper investigates the computational aspects of distributionally robust chance constrained optimization problems. In contrast to previous research that mainly focused on the linear case (with a few exceptions discussed in detail below), we consider the case where the constraints can be non-linear to the decision variable, and in particular to the uncertain parameters. This formulation is of great interest as it can model non-linear uncertainties that are ubiquitous in applications. Our main result shows that distributionally robust chance constrained optimization is tractable, provided that the uncertainty is characterized by its mean and variance, and the constraint function is concave in the decision variables, and quasi-convex in the uncertain parameters. En route, we establish an equivalence relationship between distributionally robust chance constraint and the robust optimization framework that models uncertainty in a deterministic manner. This links two broadly applied paradigms in decision making under uncertainty and extends previous results of the same spirit in the linear case to more general cases. We then consider probabilistic envelope constraints, a generalization of distributionally robust chance constraints first proposed in Xu et al. (Oper Res 60:682-700, ) for the linear case. We extend this framework to the non-linear case, and derive sufficient conditions that guarantee its tractability. Finally, we investigate tractable approximations of joint probabilistic envelope constraints, and provide the conditions when these approximation formulations are tractable. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are robust optimization methods that have been used in many engineering applications. However, real-world fitness evaluations can be computationally expensive, so it may be necessary to estimate the fitness with an approximate model. This article reviews design and analysis of computer experiments (DACE) as an approximation method that combines a global polynomial with a local Gaussian model to estimate continuous fitness functions. The article incorporates DACE in various evolutionary algorithms, to test unconstrained and constrained benchmarks, both with and without fitness function evaluation noise. The article also introduces a new evolution control strategy called update-based control that estimates the fitness of certain individuals of each generation based on the exact fitness values of other individuals during that same generation. The results show that update-based evolution control outperforms other strategies on noise-free, noisy, constrained and unconstrained benchmarks. The results also show that update-based evolution control can compensate for fitness evaluation noise. [ABSTRACT FROM AUTHOR]

The processing of signal on graphs is becoming an important emerging area that has great potential in a wide variety of applications, e.g. social network. The work by Narang and Ortega (2012) laid the framework for critically sampled orthogonal two-channel filter bank for signal on undirected bipartite graphs. The design method presented by Narang and Ortega (2012) does not allow the tailoring of the filters spectral response and the control of the reconstruction error of the filter bank. This work present a method to design spectral filters that is based on Bernstein polynomial approximation and constrained optimization. The method allows the trade-off between transition band sharpness, ripple magnitude and reconstruction error. [ABSTRACT FROM AUTHOR]

In this article, for a real Banach spaceXand a closed subspaceY, we consider aspects of proximinality, its stronger variants and the notion of centrability, Chebyshev centers, for a class of subspaces that are relatively constrained in a Banach space, in the sense that forx ∉ Y,Yis constrained inspan{x,Y}. We show that ifX, under the canonical embedding has the strong--ball property in its bidual, then the same is true ofY. We also give applications of these results to proximinality in spaces of Bochner integrable functions. We consider a class of Banach spaces for which a formula due to Smith and Ward on relative Chebyshev centers and radius is valid. We show that any locally constrained subspace of the GrothendieckG-space is aG-space. [ABSTRACT FROM AUTHOR]

This paper develops a distributionally robust joint chance constrained optimization model for a dynamic network design problem (NDP) under demand uncertainty. The major contribution of this paper is to propose an approach to approximate a joint chance-constrained Cell Transmission Model (CTM) based System Optimal Dynamic Network Design Problem with only partial distributional information of uncertain demand. The proposed approximation is tighter than two popular benchmark approximations, namely the Bonferroni's inequality and second-order cone programming (SOCP) approximations. The resultant formulation is a semidefinite program which is computationally efficient. A numerical experiment is conducted to demonstrate that the proposed approximation approach is superior to the other two approximation approaches in terms of solution quality. The proposed approximation approach may provide useful insights and have broader applicability in traffic management and traffic planning problems under uncertainty. [ABSTRACT FROM AUTHOR]

In the first part of this article optimization of polyhedral discrete and differential inclusions is considered, the problem is reduced to convex minimization problem and the necessary and sufficient condition for optimality is derived. The optimality conditions for polyhedral differential inclusions based on discrete-approximation problem according to continuous problems are formulated. In particular, boundedness of the set of adjoint discrete solutions and upper semi-continuity of the locally adjoint mapping are proved. In the second part of this article an optimization problem described by convex inequality constraint is studied. By using the equivalence theorem concerning the subdifferential calculus and approximating method necessary and sufficient condition for discrete-approximation problem with inequality constraint is established. [ABSTRACT FROM AUTHOR]

Optimal sampling inspection plans for defects per unit with fixed acceptance numbers and limiting quality levels are developed to provide the appropriate protection to customers when the number of nonconformities per sampled item follows a Poisson distribution. The best inspection scheme assures the customer, who has to judge the quality of the submitted material, that a supplier's lot is released only when there is conclusive evidence that it is satisfactory. The underlying integer nonlinear programming problem is formulated and solved in the frequentist setting, and a practically exact approximation to the minimum sample size is presented. Because there is often no reason to assume that the process average is constant, the classical perspective is then extended to those situations in which there is substantial prior information on the supplier's process. A family of generalized truncated gamma models and several restricted maximum entropy distributions satisfying typical constraints are adopted to describe the stochastic fluctuations in the process average. Optimal defects-per-unit acceptance plans are determined by solving the corresponding constrained minimization problems. Lower and upper bounds on the required sample size are deduced in closed-forms. A general procedure based on Taylor series expansions of the operating characteristic function around the mean quality level of the rejectable lots is proposed to derive an explicit, accurate, easily computable approximation to the smallest sample size that provides the required average customer protection. This procedure greatly simplifies the determination of optimal plans from defect or failure count data and prior knowledge, and also requires little prior information, namely the prior mean and variance of the quality level of the rejectable lots, which could be estimated from past data and expert opinions. The suggested methodology is applied to the manufacturing of paper and glass for illustrative purposes. Our approach allows the practitioners to incorporate into the quality analysis a reduced parameter space for the process average. Furthermore, the proposed sampling plans are reasonably insensitive to small disturbances in the prior knowledge on the process average, and the effective use of the available information on the supplier's process provides a more realistic assessment of the actual customer protection, as well as considerable savings in testing time and sample size. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider a class of nonlinear semi-infinite optimization problems. These problems involve continuous inequality constraints that need to be satisfied at every point in an infinite index set, as well as conventional equality and inequality constraints. By introducing a novel penalty function to penalize constraint violations, we form an approximate optimization problem in which the penalty function is minimized subject to only bound constraints. We then show that this penalty function is exact—that is, when the penalty parameter is sufficiently large, any local solution of the approximate problem can be used to generate a corresponding local solution of the original problem. On this basis, the original problem can be solved as a sequence of approximate nonlinear programming problems. We conclude the paper with some numerical results demonstrating the applicability of our approach to PID control and filter design. [Copyright &y& Elsevier]

Curved trajectories algorithm (CTA) is a package for the minimization of unconstrained functions of several variables with intervals on the variables. The core algorithm is novel in that steps may follow polynomial space curves instead of straight lines. The space curves result from truncations of a Taylor series expansion of the Gradient inverse function. A critical item in the efficiency of CTA is the factorization of the sparse Hessian matrix and handling a non-positive definite Hessian. This paper describes a new approach for non-positive definite Hessians that has given robust results especially for ill-conditioned problems and the CTA package compares very favourably with other minimization packages using a sample of large Cuter problems. [ABSTRACT FROM AUTHOR]

The notions of α-well-posedness and generalized α-well-posedness for a system of constrained variational inequalities involving set-valued map- pings (for short, (SCVI)) are introduced in Hilbert spaces. Existence theorems of solutions for (SCVI) are established by using penalty techniques. Met- ric characterizations of α-well-posedness and generalized α-well-posedness, in terms of the approximate solutions sets, are presented. Finally, the equiva- lences between (generalized) α-well-posedness for (SCVI) and existence and uniqueness of its solutions are also derived under quite mild assumptions. [ABSTRACT FROM AUTHOR]

Abstract: A simple three-term conjugate gradient algorithm which satisfies both the descent condition and the conjugacy condition is presented. This algorithm is a modification of the Hestenes and Stiefel algorithm (Hestenes and Stiefel, 1952) [10], or that of Hager and Zhang (Hager and Zhang, 2005) [23] in such a way that the search direction is descent and it satisfies the conjugacy condition. These properties are independent of the line search. Also, the algorithm could be considered as a modification of the memoryless BFGS quasi-Newton method. The new approximation of the minimum is obtained by the general Wolfe line search, now using a standard acceleration technique developed by Andrei (2009) [27]. For uniformly convex functions, under standard assumptions, the global convergence of the algorithm is proved. Numerical comparisons of the suggested three-term conjugate gradient algorithm versus six other three-term conjugate gradient algorithms, using a set of 750 unconstrained optimization problems, show that all these computational schemes have similar performances, the suggested one being slightly faster and more robust. The proposed three-term conjugate gradient algorithm substantially outperforms the well-known Hestenes and Stiefel conjugate gradient algorithm, as well as the more elaborate CG_DESCENT algorithm. Using five applications from the MINPACK-2 test problem collection (Averick et al., 1992) [25], with variables, we show that the suggested three-term conjugate gradient algorithm is the top performer versus CG_DESCENT. [Copyright &y& Elsevier]

Abstract: A semi-analytical model with three optimizing parameters and a novel non-Gaussian function as the fundamental modal field solution has been proposed to arrive at an accurate solution to predict various propagation parameters of graded-index fibers with less computational burden than numerical methods. In our semi analytical formulation the optimization of core parameter U which is usually uncertain, noisy or even discontinuous, is being calculated by Nelder–Mead method of nonlinear unconstrained minimizations as it is an efficient and compact direct search method and does not need any derivative information. Three optimizing parameters are included in the formulation of fundamental modal field of an optical fiber to make it more flexible and accurate than other available approximations. Employing variational technique, Petermann I and II spot sizes have been evaluated for triangular and trapezoidal-index fibers with the proposed fundamental modal field. It has been demonstrated that, the results of the proposed solution identically match with the numerical results over a wide range of normalized frequencies. This approximation can also be used in the study of doped and nonlinear fiber amplifier. [Copyright &y& Elsevier]

A method is proposed to smooth the square-order exact penalty function for inequality constrained optimization. It is shown that, under some conditions, an approximately optimal solution of the original problem can be obtained by searching an approximately optimal solution of the smoothed penalty problem. An algorithm based on the smoothed penalty functions is given. The algorithm is shown to be convergent under mild conditions. Two numerical examples show that the algorithm seems efficient. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a method to smooth exact penalty function for inequality constrained optimization. It is shown that an approximate global solution of the original problem can be obtained by searching a global solution of the smoothed penalty problem. Under some mild conditions, the method based on our smoothing function is shown to be globally convergent. Some numerical examples are given to illustrate the applicability of the present smoothing method. [Copyright &y& Elsevier]

This paper presents a methodology for constrained efficient global optimization (EGO) using support vector machines (SVMs). While the objective function is approximated using Kriging, as in the original EGO formulation, the boundary of the feasible domain is approximated explicitly as a function of the design variables using an SVM. Because SVM is a classification approach and does not involve response approximations, this approach alleviates issues due to discontinuous or binary responses. More importantly, several constraints, even correlated, can be represented using one unique SVM, thus considerably simplifying constrained problems. In order to account for constraints, this paper introduces an SVM-based 'probability of feasibility' using a new Probabilistic SVM model. The proposed optimization scheme is constituted of two levels. In a first stage, a global search for the optimal solution is performed based on the 'expected improvement' of the objective function and the probability of feasibility. In a second stage, the SVM boundary is locally refined using an adaptive sampling scheme. An unconstrained and a constrained formulation of the optimization problem are presented and compared. Several analytical examples are used to test the formulations. In particular, a problem with 99 constraints and an aeroelasticity problem with binary output are presented. Overall, the results indicate that the constrained formulation is more robust and efficient. [ABSTRACT FROM AUTHOR]

The authors discuss the statistical methodology called experiments-based interpolation technique (DoIt), which they consider to become critical in approximate Bayesian inference. They compare DoIt with variational Bayes (VB) in the areas including convenience, implementability, and flexibility and mention the advantage of VB over DoIt in terms of implementability. They add that DoIt needs various multidimensional constrained optimizations and tuning in longitudinal data analysis.

We present an extension to Jaynes' maximum entropy principle that incorporates latent variables. The principle of latent maximum entropy we propose is different from both Jaynes' maximum entropy principle and maximum likelihood estimation, but can yield better estimates in the presence of hidden variables and limited training data. We first show that solving for a latent maximum entropy model poses a hard nonlinear constrained optimization problem in general. However, we then show that feasible solutions to this problem can be obtained efficiently for the special case of log-linear models--which forms the basis for an efficient approximation to the latent maximum entropy principle. We derive an algorithm that combines expectation-maximization with iterative scaling to produce feasible log-linear solutions. This algorithm can be interpreted as an alternating minimization algorithm in the information divergence, and reveals an intimate connection between the latent maximum entropy and maximum likelihood principles. To select a final model, we generate a series of feasible candidates, calculate the entropy of each, and choose the model that attains the highest entropy. Our experimental results show that estimation based on the latent maximum entropy principle generally gives better results than maximum likelihood when estimating latent variable models on small observed data samples. [ABSTRACT FROM AUTHOR]

Abstract: We present an algorithm to generate point distributions with high-quality blue noise characteristics on discrete surfaces. It is based on the concept of Capacity-Constrained Surface Triangulation (CCST), which approximates the underlying continuous surface as a well-formed triangle mesh with uniform triangle areas. The algorithm takes a triangle mesh and the number of sample points as input, and iteratively alternates between optimization of the geometry (positions) of the points and optimization of their topology (connectivity) until convergence. Since the method is relaxation-based, it allows precise control over the number of sample points. Differential domain analysis shows that the point distribution of CCST exhibits typical blue noise characteristics, superior to other relaxation-based sampling methods and is very efficient compared to other traditional dart-throwing methods. We generalize CCST to non-uniform sampling by incorporating a density function. This can be useful in many geometry processing applications, such as curvature-aware remeshing. [Copyright &y& Elsevier]

In correlated electron materials, the application of many-body techniques for the study of interaction effects or unconventional superconductivity often requires the formulation of an effective low-energy model that contains only the relevant bands near the Fermi level. However, the bands away from the Fermi level are known to renormalize the low-energy interactions substantially. Here we compare different schemes to derive low-energy effective theories for interacting electrons in solids. The frequently used constrained random phase approximation (cRPA) is identified as a particular resummation of higher-order interaction terms that includes important virtual corrections. We then propose an adapted functional renormalization group scheme that includes the cPRA, but also allows one to go beyond the cRPA approximation. We study a simple two-band model in order to explore the differences between the approximations. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an algorithm for approximating conic sections by constrained Bézier curves of arbitrary degree is proposed. First, using the eigenvalues of recurrence equations and the method of undetermined coefficients, some exact integral formulas for the product of two Bernstein basis functions and the denominator of rational quadratic form expressing conic section are given. Then, using the least squares method, a matrix-based representation of the control points of the optimal Bézier approximation curve is deduced. This algorithm yields an explicit, arbitrary-degree Bézier approximation of conic sections which has function value and derivatives at the endpoints that match the function value and the derivatives of the conic section up to second order and is optimal in the norm. To reduce error, the method can be combined with a curve subdivision scheme. Computational examples are presented to validate the feasibility and effectiveness of the algorithm for a whole curve or its part generated by a subdivision. [Copyright &y& Elsevier]

The quadratic maximization problem with rank constraint means where n and m are two given positive integers with n ≤ m, Q is a given real symmetric matrix, is the unit sphere in and stands for the Euclidean inner product. In this note, motivated by Briët, Filho and Vallentin's and Ye's excellent works [J. Briët, F. Filho, and F. Vallentin, The positive semidefinite Grothendieck problem with rank constraint, Automata, Languages and Programming, Lecture Notes in Computer Science, Vol. 6198, 2010, pp. 31–42.; Y. Ye, Approximating quadratic programming with bound and quadratic constraints, Math. Program. 84 (1999), pp. 219–226], we first derive the relative approximation ratio for the above problem with arbitrary symmetric matrix Q. While Q was supposed to be positive semidefinite in [J. Briët, F. Filhom, and F. Vallentin, The positive semidefinite Grothendieck problem with rank constraint, Automata, Languages and Programming, Lecture Notes in Computer Science, Vol. 6198, 2010, pp. 31–42.]. Secondly, we extend such a relative approximation ratio to a generalization of the above optimization problem. Finally, a slightly improved relative approximation ratio for the optimization problem considered in [Y. Ye, Approximating quadratic programming with bound and quadratic constraints, Math. Program. 84 (1999), pp. 219–226] is presented. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper theoretical results regarding a generalized minimum rank matrix approximation problem in the spectral norm are presented. An alternative solution expression for the generalized matrix approximation problem is obtained. This alternative expression provides a simple characterization of the achievable minimum rank, which is shown to be the same as the optimal objective value of the classical problem considered by Eckart–Young–Schmidt–Mirsky, as long as the generalized problem is feasible. In addition, this paper provides a result on a constrained version of the matrix approximation problem, establishing that the later problem is solvable via singular value decomposition. [Copyright &y& Elsevier]

Abstract: The neutron matter equation of states of the so-called Bethe homework problem (NMESB) is obtained using the (extended) lowest order constrained variational ((E) LOCV), the lowest order factorized Iwamoto–Yamada (LOF) and the Fermi (hypernetted) chain (FC (FHNC)) formalisms. The FC and the FHNC approximations are performed, using the LOCV or the ELOCV correlation function. It is shown that, if the normalization constraint is satisfied, then the NMESB results by using the LOCV, the ELOCV, the FC and the FHNC formalisms, will become close together and agree well with the corresponding FHNC calculations performed by Zabolitzky (Z) with the parameterized Krotscheck and Takahashi (KT) correlation function. It is also demonstrated that the LOF and the FC calculations, evaluated by employing a parameterized correlation function, are far from the above results, particularly at high densities. Finally, in order to test the convergence of LOF approximation, the two- and the three-body normalization factors are calculated and it is shown that in the LOF approximation, the truncation of cluster expansion after the first few leading terms is not reliable (which is well known as the Emery difficulty). [Copyright &y& Elsevier]

Abstract: Constraint qualifications in terms of approximate Jacobians are investigated for a nonsmooth constrained optimization problem, in which the involved functions are continuous but not necessarily locally Lipschitz. New constraint qualifications in terms of approximate Jacobians, weaker than the generalized Robinson constraint qualification (GRCQ) in Jeyakumar and Yen [V. Jeyakumar, N.D. Yen, Solution stability of nonsmooth continuous systems with applications to cone-constrained optimization, SIAM J. Optim. 14 5 (2004) 1106–1127], are introduced and some examples are provided to show the utility of constrained qualifications introduced. Since the calmness condition is regarded as the basic condition for optimality conditions, the relationships between the constraint qualifications proposed and the calmness of solution mapping are also studied. [Copyright &y& Elsevier]