A new Liu–Storey type nonlinear conjugate gradient method for unconstrained optimization problems

Although the Liu–Storey (LS) nonlinear conjugate gradient method has a similar structure as the well-known Polak–Ribière–Polyak (PRP) and Hestenes–Stiefel (HS) methods, research about this method is very rare. In this paper, based on the memoryless BFGS quasi-Newton method, we propose a new LS type method, which converges globally for general functions with the Grippo–Lucidi line search. Moreover, we modify this new LS method such that the modified scheme is globally convergent for nonconvex minimization if the strong Wolfe line search is used. Numerical results are also reported.     

A Douglas–Rachford splitting method for solving equilibrium problems

We propose a splitting method for solving equilibrium problems involving the sum of two bifunctions satisfying standard conditions. We prove that this problem is equivalent to find a zero of the sum of two appropriate maximally monotone operators under a suitable qualification condition. Our algorithm is a consequence of the Douglas–Rachford splitting applied to this auxiliary monotone inclusion. Connections between monotone inclusions and equilibrium problems are studied.     

A quadratically convergent predictor—corrector method for solving linear programs from infeasible starting points

A predictor—corrector method for solving linear programs from infeasible starting points is analyzed. The method is quadratically convergent and can be combined with Ye's finite termination scheme under very general assumptions. If the starting points are large enough then the algorithm hasO(nL) iteration complexity. If the ratio between feasibility and optimality at the starting points is small enough then the algorithm has O( ) iteration complexity. For feasible starting points the algorithm reduces to the Mizuno—Todd—Ye predictor—corrector method.This work was supported by an interdisciplinary research grant from the Institute for Advanced Studies of the University of Iowa.     

A note on “A new method for solving fully fuzzy linear programming problems”

This note shows that solving fully fuzzy linear programming (FFLP) model presented by Kumar et al. [A. Kumar, J. Kaur, P. Singh, A new method for solving fully fuzzy linear programming problems, Appl. Math. Model. 35 (2011) 817–823] needs some corrections to make the model well in general. A new version is provided in this note. A simple example is also presented to demonstrate the new form.     

Some new perspectives for solving 0–1 integer programming problems using Balas method

Computational Management Science - Egon Balas’s additive algorithm, also known as implicit enumeration, is a technique that uses a branch-and-bound (B&B) approach to finding optimal...     

A majorization–minimization algorithm for split feasibility problems

The classical multi-set split feasibility problem seeks a point in the intersection of finitely many closed convex domain constraints, whose image under a linear mapping also lies in the intersection of finitely many closed convex range constraints. Split feasibility generalizes important inverse problems including convex feasibility, linear complementarity, and regression with constraint sets. When a feasible point does not exist, solution methods that proceed by minimizing a proximity function can be used to obtain optimal approximate solutions to the problem. We present an extension of the proximity function approach that generalizes the linear split feasibility problem to allow for non-linear mappings. Our algorithm is based on the principle of majorization–minimization, is amenable to quasi-Newton acceleration, and comes complete with convergence guarantees under mild assumptions. Furthermore, we show that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences. We explore several examples illustrating the merits of non-linear formulations over the linear case, with a focus on optimization for intensity-modulated radiation therapy.     

A generalized Laguerre–Legendre spectral collocation method for solving initial-boundary value problems

In this research, a mixed spectral collocation method based on Kronecker product is proposed for solving initial-boundary value problems. New implementation is suggested to achieve more accurate approximation at longer times. Test problems are also studied to demonstrate how this method is implemented. Numerical experiments reveal that the new method is very effective and convenient.     

A method of adding–removing knots for solving smoothing problems with obstacles

We study a method of adding–removing knots that has been proposed in the literature for solving the smoothing problem with obstacles. The method uses the coefficients of natural splines in the expansion by radial basis functions. We present examples of cycling and counterexamples to possible use of some ideas. We also give some sufficient conditions for finiteness of the method.     

On a regularized Levenberg–Marquardt method for solving nonlinear inverse problems

We consider a regularized Levenberg–Marquardt method for solving nonlinear ill-posed inverse problems. We use the discrepancy principle to terminate the iteration. Under certain conditions, we prove the convergence of the method and obtain the order optimal convergence rates when the exact solution satisfies suitable source-wise representations.     

A general framework for solving Riemann–Hilbert problems numerically

A new, numerical framework for the approximation of solutions to matrix-valued Riemann?CHilbert problems is developed, based on a recent method for the homogeneous Painlevé II Riemann?CHilbert problem. We demonstrate its effectiveness by computing solutions to other Painlevé transcendents. An implementation in Mathematica is made available online.     

A quadratic finite element method for solving biharmonic problems in ℝ n

Summary A family of simplicial finite element methods having the simplest possible structure, is introduced to solve biharmonic problems in ⁿ ,n3, using the primal variable. The family is inspired in the MORLEY triangle for the two dimensional case, and in some sense this element can be viewed as its member corresponding to the valuen=2.     

Comment on “A new three-term conjugate gradient method for unconstrained problem”

Here, necessary corrections on computing an optimal parameter of the TTDES method of Andrei (Numer. Math. 68(2), 305–321 2015) are stated in brief. Throughout, we use the same notations and equation numbers as in N.Andrei.     

A family of predictor–corrector schemes for a class of variational inequalities

This work is concerned with an abstract problem in the form of a variational inequality, or equivalently a minimization problem involving a non-differential functional. The problem is inspired by a formulation of the initial–boundary value problem of elastoplasticity. The objective of this work is to revisit the predictor–corrector algorithms that are commonly used in computational applications, and to establish conditions under which these are convergent or, at least, under which they lead to decreasing sequences of the functional for the problem. The focus is on the predictor step, given that the corrector step by definition leads to a decrease in the functional. The predictor step may be formulated as a minimization problem. Attention is given to the tangent predictor, a line search approach, the method of steepest descent, and a Newton-like method. These are all shown to lead to decreasing sequences.     

Testing optimality for quadratic 0–1 unconstrained problems

This paper analyses a necessary and sufficient optimality condition for quadratic pseudo-Boolean unconstrained problems. It is proved that in general testing any necessary and sufficient optimality condition is a difficult task for anyNP-hard problem. An-optimality condition is derived together with an approximation scheme to test it.This work has been supported by the Progetto Finalizzato Trasporti 2, 93.01799.PF74.Professor Paolo Carraresi died unexpectedly on March 5, 1994. At the time of his death this paper had been completed. While undertaking the final revision, the other two authors were reminded just how much they were indebted to Professor Carraresi after many years of common work together.     

Implementation of a modified Marder–Weitzner method for solving nonlinear eigenvalue problems

Our goal is to propose four versions of modified Marder–Weitzner methods and to present the implementation of the new-type methods with incremental unknowns for solving nonlinear eigenvalue problems. By combining with compact schemes and modified Marder–Weitzner methods, six schemes well suited for the calculation of unstable solutions are obtained. We illustrate the efficiency of the new algorithms by using numerical computations and by comparing them with existing methods for some two-dimensional problems.     

Implicit–explicit predictor–corrector schemes for nonlinear parabolic differential equations

In the present paper, a family of predictor–corrector (PC) schemes are developed for the numerical solution of nonlinear parabolic differential equations. Iterative processes are avoided by use of the implicit–explicit (IMEX) methods. Moreover, compared to the predictor schemes, the proposed methods usually have superior accuracy and stability properties. Some confirmation of these are illustrated by using the schemes on the well-known Fisher’s equation.     

A P0–P0 weak Galerkin finite element method for solving singularly perturbed reaction–diffusion problems

This paper investigates the lowest-order weak Galerkin finite element (WGFE) method for solving reaction–diffusion equations with singular perturbations in two and three space dimensions. The system of linear equations for the new scheme is positive definite, and one might readily get the well-posedness of the system. Our numerical experiments confirmed our error analysis that our WGFE method of the lowest order could deliver numerical approximations of the order O(h^1/2) and O(h) in H¹ and L² norms, respectively.