Solving 3D boundary element problems using constrained iterative approach

Targeting the adjustment of the errors-in-variables (EIV) model with equality and inequality constraints, a general solution that is similar to the classical least square adjustment is proposed based on the penalty function and the weight in measurement. Firstly, we take the equality constraints as inequality constraints that do not satisfy the constraint conditions and construct the penalty functions of equality and inequality constraints, respectively. Thus, the inequality constrained optimization problem is transformed into an unconstrained optimization problem. Then the detailed calculation formula and approximate accuracy evaluation formula of the general solution are deduced. The iteration formula of the general solution is easy regarding comprehension and applicable in implementation. It can not only solve the EIV model with equality and inequality constraints respectively, but also address the EIV model with equality and inequality constraints simultaneously. In addition, it can promote the Gauss–Markov (G-M) model with equality and inequality constraints. Finally, three examples (i.e., equality constraints, inequality constraints and those with equality and inequality constraints) are validated, indicating that the general solution is effective and feasible. The results show that the general solution is effective and feasible.

We consider a general multi-agent convex optimization problem where the agents are to collectively minimize a global objective function subject to a global inequality constraint, a global equality constraint, and a global constraint set. The objective function is defined by a sum of local objective functions, while the global constraint set is produced by the intersection of local constraint sets. In particular, we study two cases: one where the equality constraint is absent, and the other where the local constraint sets are identical. We devise two distributed primal-dual subgradient algorithms based on the characterization of the primal-dual optimal solutions as the saddle points of the Lagrangian and penalty functions. These algorithms can be implemented over networks with dynamically changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically agree on optimal solutions and optimal values of the optimization problem under the Slater's condition.

A novel nonlinear Lagrangian is presented for constrained optimization problems with both inequality and equality constraints, which is nonlinear with respect to both functions in problem and Lagrange multipliers. The nonlinear Lagrangian inherits the smoothness of the objective and constraint functions and has positive properties. The algorithm on the nonlinear Lagrangian is demonstrated to possess local and linear convergence when the penalty parameter is less than a threshold (the penalty parameter in the penalty method has to approximate zero) under a set of suitable conditions, and be super-linearly convergent when the penalty parameter is decreased following Lagrange multiplier update. Furthermore, the dual problem based on the nonlinear Lagrangian is discussed and some important properties are proposed, which fail to hold for the dual problem based on the classical Lagrangian. At last, the preliminary and comparing numerical results for several typical test problems by using the new nonlinear Lagrangian algorithm and the other two related nonlinear Lagrangian algorithms, are reported, which show that the given nonlinear Lagrangian is promising.

We present a stochastic approximation algorithm based on penalty function method and a simultaneous perturbation gradient estimate for solving stochastic optimisation problems with general inequality constraints. We present a general convergence result that applies to a class of penalty functions including the quadratic penalty function, the augmented Lagrangian, and the absolute penalty function. We also establish an asymptotic normality result for the algorithm with smooth penalty functions under minor assumptions. Numerical results are given to compare the performance of the proposed algorithm with different penalty functions.

In an earlier study, a boundary element method (BEM) and a weighted finite difference method (WFDM) were combined to analyse the flow in a water reservoir. In this paper, the BEM is developed to obtain numerically stable and convergent results for the water flow and concentration distribution in the water reservoir. In order to apply the BEM to the problem, three techniques were used. (1) The first is the penalty method, in which the pressure terms are eliminated in the Navier-Storks equations for the BEM. (2) The second involves taking the uniform velocity into the basic solution in both the flow and concentration distribution analyses of our BEM. (3) The third is to identify the kinetic and dynamic conditions for pressure against the vertical wall or the bottom and on the free surface of the reservoir because it is necessary to ascertain the pressure conditions in case that the penalty method is not used. Referring to the velocity vectors of the water flow that are calculated by the WFDM and observed in the model simulation of a water reservoir constructed in our laboratory, the effect and accuracy of the alternative BEM are estimated.

Contact force algorithm in explicit transient analysis using finite-element method

This paper discusses the application of a Modified Lagrange Multipliers Method (MLM) in solving optimization problems with equality and inequality constraints. The method is aimed at circumventing the computational rigours undergone using the Lagrange multipliers method in solving this class of problems with equality and inequality constraints independently. Also, it aims at reducing the computer memory space occupied by the independent methods in solving these problems using the said methods. In other to achieve this, a straightforward and simple to handle MLM algorithm has been developed to solve the same optimization problems with both equality and inequality constraints. Comparing the numerical results with that of the classical methods show some improvements.

This study deals with an application of constrained formulation to a nonlinear sloshing problem based on the arbitrary Lagrangian–Eulerian finite element method (ALE). The ALE method incorporates a discretized form of equations of motion with mesh updating algorithms in order to prevent a problem of mesh distortion. This paper focuses on an analytical aspect of such treatments as constrained systems in the formulation of the ALE method. Since the mesh updating algorithms give algebraic relations for nodal coordinates, this study treats these relations as constraints. Then, we introduce formulation for constrained systems based on the method of Lagrange multipliers. As a result of this formulation, equations of motion are given by differential algebraic equations (DAEs) consisting of differential equations for time evolution of physical quantities and algebraic equations (constraints). The present method can be classified into a kind of augmented formulation. Moreover, we present a size-reduction technique used in the Newton–Raphson method in order to remove a part of the redundant degrees-of-freedom in the iterative procedures, because the resulting set of DAEs involves a larger number of unknowns than the minimal number of degrees-of-freedom due to the introduction of the constrained formulation. The derived reduced system still holds the physically essential part of equations of motion for the sloshing problem. Even though it is not reduced to the minimal degrees-of-freedom, it does not involve algebraically complicated and inefficient procedures in computation. In addition, this study presents a method to introduce damping effects defined in the modal space into the FEM models. The proposed approach is validated by comparisons with experimental data in the time domain analysis.

The hybrid finite element (FE)-boundary element (BE) formulation for electromagnetic diffusion problems provides some advantages in modeling including (1) no truncation error, (2) ease of model generation, and (3) ease in solving problems involving moving conductors. However, the resulting matrix from this formulation loses its symmetry and is not banded, unlike the FE method. This results in significantly lower convergence rates in iterative solution schemes. A novel, split-domain iteration scheme (SDIS) was developed to reduce computation times. The new scheme splits the domain into FE and BE subdomains. Only the matrix associated with the FE subdomain is solved subject to initial guessed potentials along the interface of two subdomains. The BE equation is then used as the corrector to update the prescribed interface potentials. The iteration scheme proceeds with these newly corrected interface potentials until the solution converges within the required prescribed error tolerances. In this paper, detailed descriptions of the newly developed scheme and performance comparisons are presented with a sample railgun problem.

For smooth optimization problem with equality constraints, new continuously differentiable penalty function is derived. It is proved exact in the sense that local optimizers of a nonlinear program are precisely the optimizers of the associated penalty function under some nondegeneracy assumption. It is simple in the sense that the penalty function only includes the objective function and constrained functions, and it doesn’t include their gradients. This is achieved by augmenting the dimension of the program by a variable that controls the weight of the penalty terms.

This paper discusses the application of the logarithmic penalty function for the optimal synthesis of function generating mechanisms satisfying inequality and equality constraints. A new method of deriving the transmission ratio and the objective function is also presented. Numerical examples are used to illustrate the optimization technique.

This paper focuses on the penalty method, which has the potential to turn constrained problems into unconstrained ones, for distributed optimization with inequality and equality constraints. A selection principle to choose penalty gains is proposed based on value-distance ratio. Furthermore, a distributed method is developed to solve the obtained problems while its convergence is rigorously proved. Finally, an example in a numerical way demonstrates the validity of this paper.

In this paper, a class of nonlinear optimal control problems with inequality constraints is considered. Based on Karush---Kuhn---Tucker optimality conditions of nonlinear optimization problems and by constructing an error function, we define an unconstrained minimization problem. In the minimization problem, we use trial solutions for the state, Lagrange multipliers, and control functions where these trial solutions are constructed by using two-layered perceptron. We then minimize the error function using a dynamic optimization method where weights and biases associated with all neurons are unknown. The stability and convergence analysis of the dynamic optimization scheme is also studied. Substituting the optimal values of the weights and biases in the trial solutions, we obtain the optimal solution of the original problem. Several examples are given to show the efficiency of the method. We also provide two applicable examples in robotic engineering.

This paper establishes a convergence theory for an interior penalty method for a linear complementarity problem governing American option valuation. By introducing an interior penalty term, we first transform the complementarity problem into a nonlinear degenerated Black-Scholes PDE. We then prove that the PDE is uniquely solvable and its solution converges to that of the original complementarity problem. Furthermore, we demonstrate the advantages of the interior penalty method over exterior penalty methods by comparing it with an existing exterior penalty method.

Este artigo apresenta uma nova abordagem para a resolução do problema de Fluxo de Potência Ótimo Reativo. Nesta abordagem, as restrições de desigualdade são tratadas pela associação dos métodos de Barreira Modificada e Primal-Dual Barreira Logarítmica (PDBL). As restrições de desigualdade são transformadas em igualdades introduzindo variáveis de folga positivas e são relaxadas através do parâmetro de barreira. Uma função Lagrangiana é associada ao problema modificado. As condições necessárias de primeira ordem são aplicadas à função Lagrangiana, gerando um sistema de equações não-lineares, o qual é linearizado pelo método de Newton. A relaxação das variáveis de folga resulta na expansão da região factível do problema original, permitindo que os limites das restrições de desigualdade sejam atingidos. Testes numéricos utilizando os sistemas CESP-440kV e SUL-SUDESTE BRASILEIRO e um teste comparativo com o método PDBL indicam que a nova abordagem é eficiente na resolução do problema Fluxo de Potência Ótimo Reativo.