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In this paper, we investigate the method of solving partial differential equations (PDEs) using artificial neural network (ANN) structures, which have been actively applied in artificial intelligence models. The ANN model for solving PDEs offers the advantage of providing explicit and continuous solutions. However, the ANN model for solving PDEs cannot construct a conventionally solvable linear system with known matrix solvers; thus, computational speed could be a significant concern. We study the implementation of the multigrid method, developing a general concept for a coarse-grid correction method to be integrated into the ANN-PDE architecture, with the goal of enhancing computational efficiency. By developing a reduced form of the multigrid method for ANN, we demonstrate that it can be interpreted as an equivalent representation of the classic multigrid expansion. We validated the applicability of the proposed method through rigorous experiments, which included analyzing loss decay and the number of iterations along with improvements in terms of accuracy, speed, and complexity. We accomplished this by employing the gradient descent method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to update the gradients while solving the given ANN systems of PDEs. [ABSTRACT FROM AUTHOR]

The BFGS method is a common and effective method for solving unconstrained optimization problems in quasi-Newton algorithm. However, many scholars have proven that the algorithm may fail in some cases for nonconvex problems under Wolfe conditions. In this paper, an adaptive projection BFGS algorithm is proposed naturally which can solve nonconvex problems, and the following properties are shown in this algorithm: ➀ The generation of the step size α j satisfies the popular Wolfe conditions; ➁ a specific condition is proposed which has sufficient descent property, and if the current point satisfies this condition, the ordinary BFGS iteration process proceeds as usual; ➂ otherwise, the next iteration point x j + 1 is generated by the proposed adaptive projection method. This algorithm is globally convergent for nonconvex problems under the weak-Wolfe-Powell (WWP) conditions and has a superlinear convergence rate, which can be regarded as an extension of projection BFGS method proposed by Yuan et al. (J. Comput. Appl. Math. 327:274-294, 2018). Furthermore, the final numerical results and the application of the algorithm to the Muskingum model demonstrate the feasibility and competitiveness of the algorithm. [ABSTRACT FROM AUTHOR]

We present two quasi-Newton algorithms for solving unconstrained optimization problems based on two modified secant relations to get reliable approximations of the Hessian matrices of the objective function. The proposed methods make use of both gradient and function values, and utilize information from the two most recent steps in contrast to the the usual secant relation using only the latest step. We show that the modified BFGS methods based on the new secant relations are globally convergent and have a local superlinear rate of convergence. Computational experiments are made on problems from the CUTEst library. Comparative numerical results show competitiveness of the proposed methods in the sense of the Dolan–Moré performance profiles. [ABSTRACT FROM AUTHOR]

This paper presents a modification of the q-BFGS method for nonlinear unconstrained optimization problems. For this modification, we use a simple symmetric positive definite matrix and propose a new q-quasi-Newton equation, which is close to the ordinary q-quasi-Newton equation in the limiting case. This method uses only first order q-derivatives to build an approximate q-Hessian over a number of iterations. The q-Armijo-Wolfe line search condition is used to calculate step length, which guarantees that the objective function value is decreasing. This modified q-BFGS method preserves the global convergence properties of the q-BFGS method, without the convexity assumption on the objective function. Numerical results on some test problems are presented, which show that an improvement has been achieved. Moreover, we depict the numerical results through the performance profiles. [ABSTRACT FROM AUTHOR]

The BFGS method, which has great numerical stability, is one of the quasi-Newton line search methods. However, the global convergence of the BFGS method with a Wolfe line search is still an open problem for general functions. Recently, Yuan et al. (Appl. Math. Mode. 47:811–825, 2017) presented a modified weak Wolfe-Powell (WWP) line search that globally converges for general functions; however, they only partially solved this open problem. In this paper, a further modified WWP line search is proposed, and the global convergence of the BFGS method is established under suitable conditions for general functions. The numerical results show that the new line search produces more interesting results than the normal line search. Moreover, a fact engineering problem is studied with the Muskingum model to show the performance of the presented algorithm. [ABSTRACT FROM AUTHOR]

We propose a BFGS method with Wolfe line searches for unconstrained multiobjective optimization problems. The algorithm is well defined even for general nonconvex problems. Global convergence and R-linear convergence to a Pareto optimal point are established for strongly convex problems. In the local convergence analysis, if the objective functions are locally strongly convex with Lipschitz continuous Hessians, the rate of convergence is Q-superlinear. In this respect, our method exactly mimics the classical BFGS method for single-criterion optimization. [ABSTRACT FROM AUTHOR]

Prediction of pollution in water flow is a very important task. To this end, it is imperative to be able to define the uncertainty in a model prediction. This is the purpose of sensitivity analysis whose role is to identify what uncertainty in the model outputs is attributable to the model inputs (parameters in this case). Traditionally, this is achieved by running the model perturbed by many random samples in the parameter space to determine their impact on the model outputs. It provides information on how much of the output variance is controlled by each parameter of the inputs. The theoretical results related to the procedure based adjoint approach for computing a sensitivity of the response function (RF) to changes in the input source are presented in the paper. It is shown that this approach allows to compute, by one single integration of the adjoint equation over a given time interval, a sensitivity of the RF to any source located in the domain of interest. The proposed approach is applied to the 2D Saint-Venant flow equations for modelling the water pollution problem. A numerical experiment is formulated and implemented for the Thanh Nhan Lake in Hanoi for studying a sensitivity of some RF to observations. The numerical model is constructed by applying the well-known finite-volume method. Two appropriate optimization problems are introduced and solved on the basis of the BFGS algorithm. The numerical results show the efficiency of the proposed method and confirm the theoretical findings. [ABSTRACT FROM AUTHOR]

There are two problems for nonconvex functions under the weak Wolfe–Powell line search in unconstrained optimization problems. The first one is the global convergence of the Polak–Ribière–Polyak conjugate gradient algorithm and the second is the global convergence of the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton method. Many scholars have proven that the two problems do not converge, even under an exact line search. Two circle counterexamples were proposed to generate the nonconvergence of the Polak–Ribière–Polyak algorithm for the nonconvex functions under the exact line search, which inspired us to define a new technique to jump out of the circle point and obtain the global convergence. Thus, a new Polak–Ribière–Polyak algorithm is designed by the following steps. (i) Given the current point and a parabolic surface is designed; (ii) An assistant point is defined based on the current point; (iii) The assistant point is projected onto the surface to generate the next point; (iv) The presented algorithm has the global convergence for nonconvex functions with the weak Wolfe–Powell line search. A similar technique is used for the quasi-Newton method to get a new quasi-Newton algorithm and to establish its global convergence. Numerical results show that the given algorithms are more competitive than other similar algorithms. Meanwhile, the well-known hydrologic engineering application problem, called parameter estimation problem of nonlinear Muskingum model, is also done by the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper is focused on improving global convergence of the modified BFGS algorithm with Yuan-Wei-Lu line search formula. This improvement has been achieved by presenting a different line search approach and it is proved that the BFGS method with this line search converges globally if the function to be minimized has Lipschitz continuous gradients. The performance of the suggested algorithm is investigated via mathematical analysis and a simulation study. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a class of interval-valued multiobjective optimization problems (IVMOP), and formulate a certain equivalent multiobjective optimization problem (MOP). We establish that any Pareto optimal solution of the equivalent MOP is also an effective solution of IVMOP. We introduce two variants of the quasi-Newton method to solve IVMOP when every component of the objective function has second-order partial generalized Hukuhara derivatives. In the first variant, we assume that the objective function of IVMOP is strongly convex, and approximate Hessian matrix of each component of the objective function of MOP by using the well-known BFGS method. The algorithm generated by this variant exhibits superlinear convergence to a locally effective solution of IVMOP. In the second variant, we approximate the Hessian matrix of each component of MOP by applying the Levenberg–Marquardt method. Moreover, this variant can be applied to non-convex functions, and the sequence generated by this variant converges to a locally effective solution of IVMOP as well. Finally, some numerical examples are furnished to illustrate the effectiveness of our developed methodology. [ABSTRACT FROM AUTHOR]

• Formulate the no-toll equilibrium (NTE) and the congestion and environmental toll (CET) equilibrium problems with heterogeneous users as unconstrained optimization problems. • Modify the BFGS method to solve these problems. • Formulate the optimal congestion toll and CET design problems as bi-level programs. • Develop the double BFGS method to solve the two bi-level programs. We design a congestion and environmental toll (CET) scheme for the morning commute with heterogeneous users in a single OD network with parallel routes. The designed toll scheme relies upon the concept of marginal-cost pricing and is anonymous. The Henderson approach is used to model road congestion and the tolling problem to examine commuter's arrival time and route choice at the CET equilibrium (CETE). Linear interpolation is applied to approximate the emission cost function and the resulting CETE problem is formulated as an unconstrained optimization problem, which is solved by the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Unlike the existing approach, this novel approach does not require that the arrival of each group of commuters at the destination at the equilibrium follows a predetermined order, and can handle non-monotone (emission) cost function. As two special cases, no-toll equilibrium (NTE) and the congestion toll equilibrium (CTE) are also examined, and the two resultant equilibrium problems are formulated and solved by the same approach. This approach is shown to be more efficient than the existing approach. Bi-level programming models are proposed to formulate the optimal congestion toll and CET design problems, in which the CTE and CETE problems are the corresponding lower level problem. These models are solved by the double BFGS method, which uses a classical BFGS method to solve the upper level model and the proposed BFGS method to solve the lower level model. Finally, numerical examples are provided to illustrate the properties of the models and the efficiency of the proposed solution algorithms. [ABSTRACT FROM AUTHOR]

We consider some non-smooth functions and investigate the numerical behavior of the Piecewise Linear Hompotopy (PLH) method ([Bozântan, A., An implementation of the piecewise-linear homotopy algorithm for the computation of fixed points, Creat. Math. Inform., 19 (2010), No. 2, 140-148] and [Bozântan, A. and Berinde, V., Applications of the PL homotopy algorithm for the computation of fixed points to unconstrained optimization problems, Creat. Math. Inform., 22 (2013), No. 1, 41-46]). We compare the PLH method with the BFGS with inexact line search, a quasi-Newton method, having some results reported in [Lewis, A. S. and Overton, M. L., Nonsmooth optimization via BFGS, submitted to SIAM J. Optimiz, (2009)]. For most of the considered cases, the characteristics of the PLH method are quite similar to the BFGS method, that is, the PLH method converges to local minimum values and the convergence rate seems to be linear with respect to the number of function evaluations, but we also identify some issues with the PLH method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We propose a new family of distributions called the exponentiated Kumaraswamy-G class with three extra positive parameters, which generalizes the Cordeiro and de Castro's family. Some special distributions in the new classare discussed. We derive some mathematical properties of the proposedclass including explicit expressions for the quantile function, ordinary andincomplete moments, generating function, mean deviations, reliability, Rényientropy and Shannon entropy. The method of maximum likelihood is used to fit the distributions in the proposed class. Simulations are performed in orderto assess the asymptotic behavior of the maximum likelihood estimates.We illustrate its potentiality with applications to two real data sets whichshow that the extended Weibull model in the new class provides a better fit than other generalized Weibull distributions. [ABSTRACT FROM AUTHOR]

We provide a maximum entropy derivation of a new family of BFGS-like methods. Similar results are then derived for block BFGS methods. This also yields an independent proof of a result of Fletcher 1991 and its generalization to the block case. [ABSTRACT FROM AUTHOR]

A new adaptive scaled Broyden-Fletcher-Goldfarb-Shanno (BFGS) method for unconstrained optimization is presented. The third term in the standard BFGS update formula is scaled in order to reduce the large eigenvalues of the approximation to the Hessian of the minimizing function. Under the inexact Wolfe line search conditions, the global convergence of the adaptive scaled BFGS method is proved in very general conditions without assuming the convexity of the minimizing function. Using 80 unconstrained optimization test functions with a medium number of variables, the preliminary numerical experiments show that this variant of the scaled BFGS method is more efficient than the standard BFGS update or than some other scaled BFGS methods. [ABSTRACT FROM AUTHOR]

In this paper, a Riemannian BFGS method for minimizing a smooth function on a Riemannian manifold is defined, based on a Riemannian generalization of a cautious update and a weak line search condition. It is proven that the Riemannian BFGS method converges (i) globally to stationary points without assuming the objective function to be convex and (ii) superlinearly to a nondegenerate minimizer. Using the weak line search condition removes the need for information from differentiated retraction. The joint matrix diagonalization problem is chosen to demonstrate the performance of the algorithms with various parameters, line search conditions, and pairs of retraction and vector transport. A preliminary version can be found in [Numerical Mathematics and Advanced Applications: ENUMATH 2015, Lect. Notes Comput. Sci. Eng. 112, Springer, New York, 2016, pp. 627{634]. [ABSTRACT FROM AUTHOR]

The standard BFGS method plays an important role among the quasi-Newton algorithms for constrained/un-constrained optimization problems. However, Dai (2003) constructed a counterexample to demonstrate that this method may fail for non-convex functions with inexact Wolfe line searches, and Mascarenhas (2004) showed the nonconvergence of this method and other methods in the Broyden family, even with exact line search techniques. These works motivate us to try to find another way to obtain another quasi-Newton method with better convergence based on the standard BFGS formula. In this paper, four approaches are used in the designed algorithm: (1) a modified weak Wolfe–Powell line search technique is introduced; (2) if one defined condition is satisfied, then the search direction and its associated step length are accepted, and the next point is designed; (3) otherwise, a parabolic will be presented, and it is regarded as the projection surface to avoid using the failed direction, and the next point x k + 1 is generated by a projection technique; and (4) to easily obtain the global convergence of the proposed algorithm, the projection point is not used in the current BFGS update but rather for all the next iterations. The new algorithm possesses global convergence for general functions under the inexact modified weak Wolfe–Powell line search technique, and it is shown that other methods in the Broyden class also have this property. Numerical results are reported to show the performance of the given algorithm and other similar methods. [ABSTRACT FROM AUTHOR]

Phase equilibrium calculations are one of the main steps in the compositional simulation of hydrocarbon reservoirs, well tubing, oil and gas pipelines as well as separation units. The main phases in all hydrocarbon production systems from a reservoir to a stock tank are gas, oil, and water. In most studies, the aqueous phase is considered bulk and phase equilibrium calculations are conducted only on the oil and gas phases. In some conditions such as thermal processes, the solubility of water in other phases and also hydrocarbon components in the water phase cannot be ignored. The robustness of equilibrium calculations for a wide range of compositions, temperatures, and pressures, including near critical area and saddle point curve, is essential for rapid and stable simulation studies. In this study, we propose the three-phase equilibrium calculations in detail for water, oil and gas phases. All the handling steps and strategies to establish a robust and efficient phase stability testing and flash calculation are explained. Moreover, appropriate tolerance values for decision-makings in the algorithm are defined. We successfully present the effectiveness of our algorithm for some three-phase samples covering a wide range of composition and production conditions. [ABSTRACT FROM AUTHOR]

The BFGS method is one of the most effective quasi-Newton algorithms for optimization problems. However, its global convergence for general functions is still open. In this paper, under a new line search technique, this problem is solved, and it is shown that other methods in the Broyden class also possess this property. Moreover, the global convergence of the PRP method is established in the case of this new line search. Numerical results are reported to show that the new line search technique is competitive to that of the normal line search. [ABSTRACT FROM AUTHOR]

The circulating cooling water pump in a nuclear power plant operates in a high temperature environment， and the pump bearing receives a large amount of heat load， so the work reliability is of great importance. To reduce the bearing's working temperature and ensure its safe operation， a cooling device should be installed in the pump system. The structure of the bearing's axial flow cooling fan in the pump was proposed， and the optimum design method of the bearing's axial flow cooling fan in the pump was established. For the fan blades， the optimized calculation using BFGS algorithm was proceeded. A simulation verification of the system was proposed by using FLUENT， whose results showed that the design method is suitable for the bearing's axial flow cooling fan design in circulating cooling water pumps in a nuclear power plant. [ABSTRACT FROM AUTHOR]

An inverse method is developed to simultaneously estimate unknown temperature-dependent thermal conductivity and specific heat of a brass rod with knowledge of temperatures taken on the specimen. An experimental process with the brass rod is built for measuring temperatures at some locations to verify the thermal conductivity and specific heat. With known temperature data recorded from the experiment, inverse solutions were rapidly obtained through the Broyden–Fletcher–Goldfarb–Shanno (BFGS) combined simple step method. Results show that the proposed method can estimate thermal properties with low iterations and in a significantly short time compared to other methods. In addition, the estimated temperatures are in very good agreement with the measurement temperature. From experimental verification, the estimated thermal properties are quite close to values obtained by simple tests, with several cases of different heat generation magnitudes. The effect of measurement errors and locations, as well as measured point numbers, on the accuracy of inverse solutions was discussed. According to analysis results, the proposed method, an accurate and efficient method for the prediction of unknown thermal conductivity and specific heat, can be applied to accurately estimate thermophysical properties of various materials. [ABSTRACT FROM AUTHOR]

Dynamic user optimal simultaneous route and departure time choice(DUO-SRDTC) problems are usually formulated as variational inequality (VI) problems whose solution algorithms generally require continuous and monotone route travel cost functions to guarantee convergence. However, the monotonicity of the route travel cost functions can not be ensured even if the route travel time functions are monotone. In contrast to traditional formulations, this paper formulates a DUO-SRDT C problem (that can have fixed or elastic demand) as a system of nonlinear equations. The system of nonlinear equations is a function of generalized origin-destination (OD) travel costs rather than route flows and includes a dynamic user optimal (DUO) route choice subproblem with perfectly elastic demand and a quadratic programming (QP) subproblem under certain assumptions. This study also proposes a solution method based on the backtracking inexact Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, the extra gradient algorithm, and the Frank-Wolf e algorithm The BFGS method, the extra gradient algorithm, and the Frank-Wolf e algorithm are used to solve the system of nonlinear equations, the DUO route choice subproblem, and the QP subproblem, respectively. The proposed formulation and solution method can avoid the requirement of monotonicity of the route travel cost functions to obtain a convergent solution and provide a new approach with which to solve DUO-SRDTC problems. Finally, numeric examples are used to demonstrate the performance of the proposed solution method. [ABSTRACT FROM AUTHOR]

An inverse heat transfer problem is investigated in the present study by the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to predict the unknown time-dependent heat generation at the weld interface and convection heat transfer coefficient during an ultrasonic metal welding process based on the knowledge of temperature measurements taken on the horn. With known temperature data at some locations on the horn, the inverse solution was rapidly obtained by solving nonlinear direct problem, Central Finite Difference and Simple Step Method. The proposed method which did not need solving adjoint and sensitivity problem revealed the characteristics of high efficiency, lower iterations for a computational algorithm and high accuracy for estimating values even when measurement error was considered. Besides, a comparison of the BFGS method with some previous methods (i.e. CGM, SCGM) was established. These results show that an excellent estimation on interfacial heat generation (or temperature), as well as a convection heat transfer coefficient, can be simultaneously obtained in this study. The current methodology will provide a useful tool to optimize welding conditions in ultrasonic welding. [ABSTRACT FROM AUTHOR]

In this paper we present several representation theorems and averaging theorems for members of the difference class of secant updates introduced by Brodlie et al. (J Inst Math Appl 11:73-82, ). Major contributions are that the integral form of the mean-value theorem leads to a proof that the BFGS update is pointwise the infinite average of all the updates on the one-dimension manifold in the Dennis class that connects the DFP secant update to the Greenstadt update, and that it can be expressed as the pointwise average of these latter two updates. Analogous results hold for all secant updates that belong to the difference class. These results contribute new understanding of the structural properties of the highly popular BFGS secant update and other updates from the difference class. [ABSTRACT FROM AUTHOR]

A matrix with zero diagonal is called a Euclidean distance matrix when the matrix values are measurements of distances between points in a Euclidean space. Because of data errors such a matrix may not be exactly Euclidean and it is desirable in many applications to find the best Euclidean matrix which approximates the non-Euclidean matrix. In this paper the problem is formulated as a smooth unconstrained minimization problem, for which rapid convergence can be obtained. Comparative numerical results are reported. [ABSTRACT FROM AUTHOR]

In this article, a class of nonconvex unconstrained optimization problems is considered. As the Armijo line search is less costing in finding a steplength, a new Armijo-type line search (called WALS) with desirable features of the Wolfe condition is employed in the proposed modified BFGS method. A new updating formula incorporated with WALS is constructed and generates approximate Hessian matrices which are positive definite. On this basis, a class of well-defined modified BFGS algorithms is developed. It shows that under some suitable conditions, the modified BFGS algorithm is globally convergent. Numerical experiments are carried out on 20 benchmark test problems and the obtained results clearly indicate the effectiveness of the developed algorithm over two most popular BFGS-type algorithms available in the literature. [ABSTRACT FROM PUBLISHER]

SUMMARY Steepest descent preconditioning is considered for the recently proposed nonlinear generalized minimal residual (N-GMRES) optimization algorithm for unconstrained nonlinear optimization. Two steepest descent preconditioning variants are proposed. The first employs a line search, whereas the second employs a predefined small step. A simple global convergence proof is provided for the N-GMRES optimization algorithm with the first steepest descent preconditioner (with line search), under mild standard conditions on the objective function and the line search processes. Steepest descent preconditioning for N-GMRES optimization is also motivated by relating it to standard non-preconditioned GMRES for linear systems in the case of a standard quadratic optimization problem with symmetric positive definite operator. Numerical tests on a variety of model problems show that the N-GMRES optimization algorithm is able to very significantly accelerate convergence of stand-alone steepest descent optimization. Moreover, performance of steepest-descent preconditioned N-GMRES is shown to be competitive with standard nonlinear conjugate gradient and limited-memory Broyden-Fletcher-Goldfarb-Shanno methods for the model problems considered. These results serve to theoretically and numerically establish steepest-descent preconditioned N-GMRES as a general optimization method for unconstrained nonlinear optimization, with performance that appears promising compared with established techniques. In addition, it is argued that the real potential of the N-GMRES optimization framework lies in the fact that it can make use of problem-dependent nonlinear preconditioners that are more powerful than steepest descent (or, equivalently, N-GMRES can be used as a simple wrapper around any other iterative optimization process to seek acceleration of that process), and this potential is illustrated with a further application example. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this article, we propose a non monotone backtracking line search technique combining with BFGS (Broyden et al., 1970) method for solving regression problems. The convergent results of the given method are established under mild conditions. Numerical results show that the proposed algorithm is interesting. [ABSTRACT FROM AUTHOR]

In this article, we propose a BFGS method for solving symmetric nonlinear equations. The presented method possesses some favourable properties: (a) the generated sequence of iterates is norm descent; (b) the generated sequence of the quasi-Newton matrix is positive definite and (c) this method possesses the global convergence and superlinear convergence. Numerical results show that the presented method is interesting. [ABSTRACT FROM PUBLISHER]

Abstract: Nonlinear equations in mathematical physics and engineering are solved by linearizing the equations and forming various iterative procedures, then executing the numerical simulation. For strongly nonlinear problems, the solution obtained in the iterative process can diverge due to numerical instability. As a result, the application of numerical simulation for strongly nonlinear problems is limited. Helicopter aeroelasticity involves the solution of systems of nonlinear equations in a computationally expensive environment. Reliable solution methods which do not need Jacobian calculation at each iteration are needed for this problem. In this paper, a comparative study is done by incorporating different methods for solving the nonlinear equations in helicopter trim. Three different methods based on calculating the Jacobian at the initial guess are investigated. [Copyright &y& Elsevier]

We first catalogue several classes of secant updates including the Dennis class, the Davidon class, and the class of all symmetric rank-2 updates. Reflection on the parametric form of this class leads to a maximality property of the Davidon class and to a natural derivation of the Broyden–Fletcher–Goldfarb–Shanno update, analogous to that presented by Fletcher for the Davidon–Fletcher–Powell inverse update. Next, we propose a symmetric rank-1 extension process for classes of updates that allows us to introduce a degree of commonness to the material presented. An application of the symmetric rank-1 extension process to the class of rank-1 secant updates produces the Dennis class, an application to the Dennis class produces the Davidon class, and an application to the Davidon class leaves the Davidon class fixed implying that it is a maximal class. The maximality of the Davidon class, the definition of the symmetric rank-1 extension process, and the demonstration that this extension process can be used to unify an important part of the literature on update classes are the contributions of the paper. [ABSTRACT FROM PUBLISHER]

Abstract: This paper investigates several gradient local search methods as an enhancement to Part I, to further improve the computational efficiency while achieving the same identification accuracy. The present study is significant in several ways. First, this study reveals the characteristics of structural identification from the optimization perspective. Second, the “switch point” from global search to local search is determined with due consideration in convergence speed and avoidance of local optima. Finally, the combined strategy based on Part I and Part II with a particular local search (BFGS) is found to achieve substantial improvement in identification efficiency and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we scale the quasiNewton equation and propose a spectral scaling BFGS method. The method has a good selfcorrecting property and can improve the behavior of the BFGS method. Compared with the standard BFGS method, the single-step convergence rate of the spectral scaling BFGS method will not be inferior to that of the steepest descent method when minimizing an n-dimensional quadratic function. In addition, when the method with exact line search is applied to minimize an n-dimensional strictly convex function, it terminates within n steps. Under appropriate conditions, we show that the spectral scaling BFGS method with Wolfe line search is globally and R-linear convergent for uniformly convex optimization problems. The reported numerical results show that the spectral scaling BFGS method outperforms the standard BFGS method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a modified BFGS (Broyden–Fletcher–Goldfarb–Shanno) method with nonmonotone line search for unconstrained optimization. Under some mild conditions, we show that the method is globally convergent without a convexity assumption on the objective function. We also report some preliminary numerical results to show the efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: One class of the lately developed methods for solving optimization problems are filter methods. In this paper we attached a multidimensional filter to the Gauss–Newton-based BFGS method given by Li and Fukushima [D. Li, M. Fukushima, A globally and superlinearly convergent Gauss–Newton-based BFGS method for symmetric nonlinear equations, SIAM Journal of Numerical Analysis 37(1) (1999) 152–172] in order to reduce the number of backtracking steps. The proposed filter method for unconstrained minimization problems converges globally under the standard assumptions. It can also be successfully used in solving systems of symmetric nonlinear equations. Numerical results show reasonably good performance of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: A BFGS method, in association with a new backtracking line search technique, is presented for solving symmetric nonlinear equations. The global and superlinear convergences of the given method are established under mild conditions. Preliminary numerical results show that the proposed method is better than the normal technique for the given problems. [Copyright &y& Elsevier]

We prove the superlinear convergence of a nonmonotone BFGS algorithm on convex objective functions under suitable conditions. [ABSTRACT FROM AUTHOR]

In this article, the KKT system of the variational inequality problem is reformulated as a nonsmooth equation. On the basis of this reformulation, a norm descent BFGS method is proposed. The method is globally and superlinearly convergent. [ABSTRACT FROM AUTHOR]

In this paper, by using a so-called fixed steplength strategy, we propose a BFGS method without use of line searches for unconstrained optimization. Under mild conditions, we show the global and superlinear convergence of the proposed method even when the objective function is nonconvex. [ABSTRACT FROM AUTHOR]

Limited-memory BFGS quasi-Newton methods approximate the Hessian matrix of second derivatives by the sum of a diagonal matrix and a .xed number of rank-one matrices. These methods are particularly effective for large problems in which the approximate Hessian cannot be stored explicitly. It can be shown that the conventional BFGS method accumulates approximate curvature in a sequence of expanding subspaces. This allows an approximate Hessian to be represented using a smaller reduced matrix that increases in dimension at each iteration. When the number of variables is large, this feature may be used to define limited-memory reduced-Hessian methods in which the dimension of the reduced Hessian is limited to save storage. Limited-memory reduced-Hessian methods have the benefit of requiring half the storage of conventional limited-memory methods. In this paper, we propose a particular reduced-Hessian method with substantial computational advantages compared to previous reduced-Hessian methods. Numerical results from a set of unconstrained problems in the CUTE test collection indicate that our implementation is competitive with the limited-memory codes L-BFGS and L-BFGS-B. [ABSTRACT FROM AUTHOR]

• Estimate impedance parameters and uncalibrated environment position simultaneously. • Consider the switching controller discontinuities and force measurement threshold. • Performance of the controller is used to evaluate the proposed estimation algorithm. This paper examines the problem of online estimation of the environment impedance parameters and position during the contact of a robot with an uncalibrated environment. Position controllers are commonly used for free motion, while force controllers are used for constrained motion of robots. However, it is not trivial to set controller parameters for practical contact tasks with unknown environments. In particular, the environmental position is uncalibrated, and it is impossible to determine the contact position through force sensor information because of the presence of a force measurement threshold. To this end, an estimation algorithm based on L-BFGS is proposed to obtain the environmental impedance parameters and position. The estimated environment position is updated with the impedance parameters, and the impedance parameter estimation is directly related to the penetration position. The penetration position is determined by the real position of manipulators and environment surface position. The estimated environmental parameters are used to modify the position and force switching controller parameters; therefore, the accuracy of these parameters can be evaluated by the performance of the modified controller. Simulations and experiments are successfully conducted and validated with the proposed impedance estimation method. The accuracy and effectiveness of the algorithm is verified through the force and trajectory tracking performance in simulations and experiments. [ABSTRACT FROM AUTHOR]

In this paper, we present a Gauss­Newton-based BFGS method for solving symmetric nonlinear equations which contain, as a special case, an unconstrained optimization problem, a saddle point problem, and an equality constrained optimization problem. A suitable line search is proposed with which the presented BFGS method exhibits an approximate norm descent property. Under appropriate conditions, global convergence and superlinear convergence of the method are established. The numerical results show that the proposed method is successful. [ABSTRACT FROM AUTHOR]

Abstract: In this note, we point out that in [Y. Xiao, H. Sun, Z. Wang, A globally convergent BFGS method with nonmonotone line search for non-convex minimization, J. Comput. Appl. Math. 230 (2009) 95–106] are not enough to guarantee the global convergence result Theorem 3.1 in . We present a new assumption and show that the proposed method in still converges globally under these three assumptions. [Copyright &y& Elsevier]

Variants of the Newton method are very popular for solving unconstrained optimization problems. The study on global convergence of the BFGS method has also made good progress. The q-gradient reduces to its classical version when q approaches 1. In this paper, we propose a quantum-Broyden–Fletcher–Goldfarb–Shanno algorithm where the Hessian is constructed using the q-gradient and descent direction is found at each iteration. The algorithm presented in this paper is implemented by applying the independent parameter q in the Armijo–Wolfe conditions to compute the step length which guarantees that the objective function value decreases. The global convergence is established without the convexity assumption on the objective function. Further, the proposed method is verified by the numerical test problems and the results are depicted through the performance profiles. [ABSTRACT FROM AUTHOR]

• Three different implementation strategies of phase-field damage models in Abaqus are addressed. • The sources codes and input files of all these three implementation schemes can be open accessed. • Representative examples validating the implementation strategies and source codes are presented. • The UEL subroutine together with the BFGS monolithic solver is of best numerical performances. • The UMAT subroutine is viable if the BFGS solver is available in coupled thermal–stress analyses. Despite the versatility in modeling complex crack configurations, phase-field damage models for fracture usually count only on in-house codes, greatly restricting their potential applications. It is thus of vital importance to implement them in those commonly used commercial software packages like Abaqus. However, so far only the less robust Newton's monolithic algorithm or the inefficient staggered solver has been considered. In this work, taking the unified phase-field damage theory (Wu, 2017) as the particular example, we present three distinct strategies of implementing phase-field damage models into Abaqus: (i) UMAT-Newton-M: a thermo-mechanically coupled user-defined material (UMAT) implementation of the modified Newton monolithic solver; (ii) UEL-Staggered: a novel user-defined element (UEL) implementation of the iterative staggered (alternate minimization) algorithm with dummy dofs; (iii) UEL-BFGS: a UEL implementation of the recently advocated BFGS quasi-Newton monolithic algorithm. The aforesaid implementation strategies are then validated against several representative benchmark problems of brittle fracture and quasi-brittle failure. It is found that, the UMAT-Newton-M implementation is the simplest but not robust enough, while the UEL-Staggered implementation is robust but extremely inefficient. Comparatively, in all cases the UEL-BFGS scheme is of the best numerical performance with lest iterations and sufficient robustness. For the sake of reproducibility of the presented numerical results and the promotion of phase-field damage models, the source codes (programmed in the free format syntax of FORTRAN90) are also provided and interested users can download them at https://github.com/jianyingwu/pfczm-abaqus. [ABSTRACT FROM AUTHOR]

Despite the popularity in modeling complex and arbitrary crack configurations in solids, phase-field damage models suffer from burdensome computational cost. This issue arises largely due to the robust but inefficient alternating minimization (AM) or staggered algorithm usually employed to solve the coupled damage–displacement governing equations. Aiming to tackle this difficulty, we propose in this work, for the first time , to use the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to solve in a monolithic manner the system of coupled governing equations, rather than the standard Newton one which is notoriously poor for problems involving non-convex energy functional. It is found that, the BFGS algorithm yields identical results to the AM/staggered solver, and is also robust for both brittle fracture and quasi-brittle failure with a single or multiple cracks. However, much less iterations are needed to achieve convergence. Furthermore, as the system matrix is less reformed per increment, the quasi-Newton monolithic algorithm is much more efficient than the AM/staggered solver. Representative numerical examples show that the saving in CPU time is about factor 3 ∼ 7 , and the larger the problem is, the more saving it gains. As the BFGS monolithic algorithm has been incorporated in many commercial software packages, it can be easily implemented and is thus attractive in the phase-field damage modeling of localized failure in solids. • A robust and efficient quasi-Newton monolithic algorithm is proposed for phase-field damage models. • The robustness comes from the BFGS method with a symmetric and positive-definite stiffness matrix. • The proposed quasi-Newton monolithic algorithm is about 3 ∼ 7 times faster than the staggered solver. • The phase-field regularized cohesive zone model is not numerically affected by the solution algorithm. [ABSTRACT FROM AUTHOR]