A new interior-point algorithm based on modified Nesterov–Todd direction for symmetric cone linear complementarity problem

We present a new primal-dual path-following interior-point algorithm for linear complementarity problem over symmetric cones. The algorithm is based on a reformulation of the central path for finding the search directions. For a full Nesterov–Todd step feasible interior-point algorithm based on the search directions, the complexity bound of the algorithm with the small update approach is the best available.     

A variant of Newton’s method based on Simpson’s three-eighths rule for nonlinear equations

We propose a new variant of Newton’s method based on Simpson’s three-eighth rule. It can be shown that the new method is cubically convergent.     

A new method of proof of Filippov’s theorem based on the viability theorem

Filippov??s theorem implies that, given an absolutely continuous function y: [t ₀; T] ?? ? ^d and a set-valued map F(t, x) measurable in t and l(t)-Lipschitz in x, for any initial condition x ₀, there exists a solution x(·) to the differential inclusion x??(t) ?? F(t, x(t)) starting from x ₀ at the time t ₀ and satisfying the estimation $$\left| {x(t) - y(t)} \right| \leqslant r(t) = \left| {x_0 - y(t_0 )} \right|e^{\int_{t_0 }^t {l(s)ds} } + \int_{t_0 }^t \gamma (s)e^{\int_s^t {l(\tau )d\tau } } ds,$$ where the function ??(·) is the estimation of dist(y??(t), F(t, y(t))) ?? ??(t). Setting P(t) = {x ?? ? ⁿ : |x ?y(t)| ?? r(t)}, we may formulate the conclusion in Filippov??s theorem as x(t) ?? P(t). We calculate the contingent derivative DP(t, x)(1) and verify the tangential condition F(t, x) ?? DP(t, x)(1) ?? ?. It allows to obtain Filippov??s theorem from a viability result for tubes.     

A new mixed finite element method based on the Crank-Nicolson scheme for Burgers’ equation

In this paper, a new mixed finite element method is used to approximate the solution as well as the flux of the 2D Burgers’ equation. Based on this new formulation, we give the corresponding stable conforming finite element approximation for the P₀² ? P₁ pair by using the Crank-Nicolson time-discretization scheme. Optimal error estimates are obtained. Finally, numerical experiments show the efficiency of the new mixed method and justify the theoretical results.     

Barzilai and Borwein’s method for multiobjective optimization problems

The present study is an attempt to extend Barzilai and Borwein’s method for dealing with unconstrained single objective optimization problems to multiobjective ones. As compared with Newton, Quasi-Newton and steepest descent multi-objective optimization methods, Barzilai and Borwein multiobjective optimization (BBMO) method requires simple and quick calculations in that it makes no use of the line search methods like the Armijo rule that necessitates function evaluations at each iteration. It goes without saying that the innovative aspect of the current study is due to the use of no function evaluations in comparison with other multi-objective optimization non-parametric methods (e.g. Newton, Quasi-Newton and steepest descent methods, to name a few) that have been investigated so far. Also, the convergence of the BBMO method for the objective functions assumed to be twice continuously differentiable has been proved. MATLAB software was utilized to implement the BBMO method, and the results were compared with the other methods mentioned earlier. Using some performance assessment, the quality of nondominated frontier of BBMO was analogized to above mentioned methods. In addition, the approximate nondominated frontiers gained from the methods were compared with the exact nondominated frontier for some problems. Also, performance profiles are considered to visualize numerical results presented in tables.     

Convergence analysis for the Secant method based on new recurrence relations

A new convergence theorem for the Secant method in Banach spaces based on new recurrence relations is established for approximating a solution of a nonlinear operator equation.It is assumed that the divided difference of order one of the nonlinear operator is Lipschitz continuous.The convergence conditions differ from some existing ones and are easily satisfied.The results of the paper are justified by numerical examples that cannot be handled by earlier works.     

A smoothing-out technique for min—max optimization

In this paper, we suggest approximations for smoothing out the kinks caused by the presence of max or min operators in many non-smooth optimization problems. We concentrate on the continuous-discrete min—max optimization problem. The new approximations replace the original problem in some neighborhoods of the kink points. These neighborhoods can be made arbitrarily small, thus leaving the original objective function unchanged at almost every point ofR ⁿ . Furthermore, the maximal possible difference between the optimal values of the approximate problem and the original one, is determined a priori by fixing the value of a single parameter. The approximations introduced preserve properties such as convexity and continuous differentiability provided that each function composing the original problem has the same properties. This enables the use of efficient gradient techniques in the solution process. Some numerical examples are presented.     

A new analytical technique to solve Fredholm’s integral equations

This paper shows that the homotopy analysis method, the well-known method to solve ODEs and PDEs, can be applied as well as to solve linear and nonlinear integral equations with high accuracy. Comparison of the present method with Adomian decomposition method (ADM), which is well-known in solving integral equations, reveals that the ADM is only special case of the present method. Also, some linear and nonlinear examples are presented to show high efficiency and illustrate the steps of the problem resolution.     

A primal–dual regularized interior-point method for convex quadratic programs

Interior-point methods in augmented form for linear and convex quadratic programming require the solution of a sequence of symmetric indefinite linear systems which are used to derive search directions. Safeguards are typically required in order to handle free variables or rank-deficient Jacobians. We propose a consistent framework and accompanying theoretical justification for regularizing these linear systems. Our approach can be interpreted as a simultaneous proximal-point regularization of the primal and dual problems. The regularization is termedexact to emphasize that, although the problems are regularized, the algorithm recovers a solution of the original problem, for appropriate values of the regularization parameters.     

A new domain decomposition algorithm for generalized Burger’s–Huxley equation based on Chebyshev polynomials and preconditioning

In this study, we use the spectral collocation method using Chebyshev polynomials for spatial derivatives and fourth order Runge–Kutta method for time integration to solve the generalized Burger’s–Huxley equation (GBHE). To reduce round-off error in spectral collocation (pseudospectral) method we use preconditioning. Firstly, theory of application of Chebyshev spectral collocation method with preconditioning (CSCMP) and domain decomposition on the generalized Burger’s–Huxley equation presented. This method yields a system of ordinary differential algebric equations (DAEs). Secondly, we use fourth order Runge–Kutta formula for the numerical integration of the system of DAEs. The numerical results obtained by this way have been compared with the exact solution to show the efficiency of the method.     

Stochastic Nelder–Mead simplex method – A new globally convergent direct search method for simulation optimization

Nelder–Mead simplex method (NM), originally developed in deterministic optimization, is an efficient direct search method that optimizes the response function merely by comparing function values. While successful in deterministic settings, the application of NM to simulation optimization suffers from two problems: (1) It lacks an effective sample size scheme for controlling noise; consequently the algorithm can be misled to the wrong direction because of noise, and (2) it is a heuristic algorithm; the quality of estimated optimal solution cannot be quantified. We propose a new variant, called Stochastic Nelder–Mead simplex method (SNM), that employs an effective sample size scheme and a specially-designed global and local search framework to address these two problems. Without the use of gradient information, SNM can handle problems where the response functions are nonsmooth or gradient does not exist. This is complementary to the existing gradient-based approaches. We prove that SNM can converge to the true global optima with probability one. An extensive numerical study also shows that the performance SNM is promising and is worthy of further investigation.     

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.     

Kantorovich’s majorants principle for Newton’s method

We prove Kantorovich’s theorem on Newton’s method using a convergence analysis which makes clear, with respect to Newton’s method, the relationship of the majorant function and the non-linear operator under consideration. This approach enables us to drop out the assumption of existence of a second root for the majorant function, still guaranteeing Q-quadratic convergence rate and to obtain a new estimate of this rate based on a directional derivative of the derivative of the majorant function. Moreover, the majorant function does not have to be defined beyond its first root for obtaining convergence rate results. The research of O.P. Ferreira was supported in part by FUNAPE/UFG, CNPq Grant 475647/2006-8, CNPq Grant 302618/2005-8, PRONEX–Optimization(FAPERJ/CNPq) and IMPA. The research of B.F. Svaiter was supported in part by CNPq Grant 301200/93-9(RN) and by PRONEX–Optimization(FAPERJ/CNPq).