A primal-dual algorithm for a constrained Fermat-Weber problem involving mixed norms
A primal-dual integral method in global optimization
Using the Fenchel conjugate of Phú’s Volume function F of a given essentially bounded measurable function f defined on the bounded box D ⊂ Rⁿ, the integral method of Chew and Zheng for global optimization is modified to a superlinearly convergent method with respect to the level sequence. Numerical results are given for low dimensional functions with a strict global essential supremum.
A projected Hessian Gauss-Newton algorithm for solving systems of nonlinear equations and inequalities.
A Projected Newton Method for Minimization Problems with Nonlinear Inequality Constraints.
A proximal ANLS algorithm for nonnegative tensor factorization with a periodic enhanced line search
The Alternating Nonnegative Least Squares (ANLS) method is commonly used for solving nonnegative tensor factorization problems. In this paper, we focus on algorithmic improvement of this method. We present a Proximal ANLS (PANLS) algorithm to enforce convergence. To speed up the PANLS method, we propose to combine it with a periodic enhanced line search strategy. The resulting algorithm, PANLS/PELS, converges to a critical point of the nonnegative tensor factorization problem under mild conditions....
A PVT-Type Algorithm for Minimizing a Nonsmooth Convex Function
2000 Mathematics Subject Classification: 90C25, 68W10, 49M37.A general framework of the (parallel variable transformation) PVT-type algorithm, called the PVT-MYR algorithm, for minimizing a non-smooth convex function is proposed, via the Moreau-Yosida regularization. As a particular scheme of this framework an ε-scheme is also presented. The global convergence of this algorithm is given under the assumptions of strong convexity of the objective function and an ε-descent condition determined by an...
A Quadratic Approximation Method for Minimizing a Class of Quasidifferentiable Functions.
A random search algorithm for finding minimum
A Regularized Continuous Linearization Method of the Fourth Order
A self-adaptive trust region method for the extended linear complementarity problems
By using some NCP functions, we reformulate the extended linear complementarity problem as a nonsmooth equation. Then we propose a self-adaptive trust region algorithm for solving this nonsmooth equation. The novelty of this method is that the trust region radius is controlled by the objective function value which can be adjusted automatically according to the algorithm. The global convergence is obtained under mild conditions and the local superlinear convergence rate is also established under...
A semifeasible trust-region model algorithm for minimization with inequality constraint.
A semi-smooth Newton method for solving elliptic equations with gradient constraints
Semi-smooth Newton methods for elliptic equations with gradient constraints are investigated. The one- and multi-dimensional cases are treated separately. Numerical examples illustrate the approach and as well as structural features of the solution.
A semi-smooth Newton method for solving elliptic equations with gradient constraints
Semi-smooth Newton methods for elliptic equations with gradient constraints are investigated. The one- and multi-dimensional cases are treated separately. Numerical examples illustrate the approach and as well as structural features of the solution.
A sensitivity-based extrapolation technique for the numerical solution of state-constrained optimal control problems
Sensitivity analysis (with respect to the regularization parameter) of the solution of a class of regularized state constrained optimal control problems is performed. The theoretical results are then used to establish an extrapolation-based numerical scheme for solving the regularized problem for vanishing regularization parameter. In this context, the extrapolation technique provides excellent initializations along the sequence of reducing regularization parameters. Finally, the favorable numerical behavior...
A smoothing Levenberg-Marquardt method for the complementarity problem over symmetric cone
In this paper, we propose a smoothing Levenberg-Marquardt method for the symmetric cone complementarity problem. Based on a smoothing function, we turn this problem into a system of nonlinear equations and then solve the equations by the method proposed. Under the condition of Lipschitz continuity of the Jacobian matrix and local error bound, the new method is proved to be globally convergent and locally superlinearly/quadratically convergent. Numerical experiments are also employed to show that...
A Systematic Approach to the Synthesis of Algorithms.
A thermodynamically motivated optimization algorithm: Circular wheel balance optimization
The author investigates a Monte Carlo algorithm for finding suboptimal solutions for a wide clase of complicated optimization problems characterized by a large combinatorial complexity. This algorithm was applied to one specific problem: circular wheel balance optimization. The slow increase of the effort along with the increasing size of the problems and the generality of the method promise that the thermodynamically motivated optimization will become a very universal and effective optimization...
A trust-region-based BFGS method with line search technique for symmetric nonlinear equations.
A Variable Metric Algorithm for Unconstrained Minimization Without Evaluation of Derivatives.
Abschätzungen bei Variationsmethoden mit Hilfe von Dualitätssätzen. II.