Unconstrained optimization reformulation of the generalized nonlinear complementarity problem and related method

In this article, we first propose an unconstrained optimization reformulation of the generalized nonlinear complementarity problem (GNCP) over a polyhedral cone, and then discuss the conditions under which its any stationary point is a solution of the GNCP. The conditions which guarantee the nonsingularity and positive definiteness of the Hessian matrix of the objective function are also given. In the end, we design a Newton-type method to solve the GNCP and show the global and local quadratic convergence of the proposed method under certain assumptions.     

Superlinear/quadratic smoothing Broyden-like method for the generalized nonlinear complementarity problem

In this article, we first reformulate the generalized nonlinear complementarity problem (GNCP) over a polyhedral cone as a smoothing system of equations and then suggest a smoothing Broyden-like method for solving it. The proposed algorithm has to solve only one system of nonhomogeneous linear equations, perform only one line search and update only one matrix per iteration. We show that the iteration sequence generated by the proposed algorithm converges globally and superlinearly under suitable conditions. Furthermore, the algorithm has local quadratic convergence under mild assumptions. Some numerical examples are given to illustrate the performance and efficiency of the presented algorithm.     

Analysis of mathematical programming problems prior to applying the simplex algorithm

Large practical linear and integer programming problems are not always presented in a form which is the most compact representation of the problem. Such problems are likely to posses generalized upper bound(GUB) and related structures which may be exploited by algorithms designed to solve them efficiently. The steps of an algorithm which by repeated application reduces the rows, columns, and bounds in a problem matrix and leads to the freeing of some variables are first presented. The ‘unbounded solution’ and ‘no feasible solution’ conditions may also be detected by this. Computational results of applying this algorithm are presented and discussed. An algorithm to detect structure is then described. This algorithm identifies sets of variables and the corresponding constraint relationships so that the total number of GUB-type constraints is maximized. Comparisons of computational results of applying different heuristics in this algorithm are presented and discussed.     

A new algorithm for generalized fractional programs

A new dual problem for convex generalized fractional programs with no duality gap is presented and it is shown how this dual problem can be efficiently solved using a parametric approach. The resulting algorithm can be seen as “dual” to the Dinkelbach-type algorithm for generalized fractional programs since it approximates the optimal objective value of the dual (primal) problem from below. Convergence results for this algorithm are derived and an easy condition to achieve superlinear convergence is also established. Moreover, under some additional assumptions the algorithm also recovers at the same time an optimal solution of the primal problem. We also consider a variant of this new algorithm, based on scaling the “dual” parametric function. The numerical results, in case of quadratic-linear ratios and linear constraints, show that the performance of the new algorithm and its scaled version is superior to that of the Dinkelbach-type algorithms. From the computational results it also appears that contrary to the primal approach, the “dual” approach is less influenced by scaling. This research was carried out at the Econometric Institute, Erasmus University, Rotterdam, the Netherlands and was supported by J.N.I.C.T. (Portugal) under contract BD/707/90-RM.     

Maximum network flows with concave gains

This paper deals with a generalized maximum flow problem with concave gains, which is a nonlinear network optimization problem. Optimality conditions and an algorithm for this problem are presented. The optimality conditions are extended from the corresponding results for the linear gain case. The algorithm is based on the scaled piecewise linear approximation and on the fat path algorithm described by Goldberg, Plotkin and Tardos for linear gain cases. The proposed algorithm solves a problem with piecewise linear concave gains faster than the naive solution by adding parallel arcs. Supported by a Grant-in-Aid for Scientific Research (No. 13780351 and No.14380188) from The Ministry of Education, Culture, Sports, Science and Technology of Japan.     

Numerical solution and optimal control for fractional tumor immune model

In this article, the numerical model of fractional tumor immunity has been described. We have proved and analyzed the model does have a stable solution. In addition to this, the optimal control of their form as well as the numerical approach for the simulation of the control problem, are both brought up and examined. We have presented evidence that demonstrates the existence of the solution. We use an algorithm modeled after the generalized Adams-Bashforth-Moulton style (GABMS) to solve the fractional tumor immune model. This amendment is predicated on changing the form to a memristive one for the first time because such a notion is being utilized for the first time to control this ailment. The dissection results have been interpreted using numerical simulations we created. To calculate the results, we relied on the Maple 15 programming language.     

A generalized Lagrangian function and multiplier method

As is well known, a saddle point for the Lagrangian function, if it exists, provides a solution to a convex programming problem; then, the values of the optimal primal and dual objective functions are equal. However, these results are not valid for nonconvex problems.In this paper, several results are presented on the theory of the generalized Lagrangian function, extended from the classical Lagrangian and the generalized duality program. Theoretical results for convex problems also hold for nonconvex problems by extension of the Lagrangian function. The concept of supporting hypersurfaces is useful to add a geometric interpretation to computational algorithms. This provides a basis to develop a new algorithm.     

Numerical solution of Burgers-Fisher equation by cubic B-spline quasi-interpolation

In this paper, numerical solution of Burgers-Fisher equation is presented based on the cubic B-spline quasi-interpolation. At first, the generalized Burgers-Fisher equation and the cubic B-spline quasi-interpolation are introduced. Moreover, the numerical scheme is presented, by using the derivative of the quasi-interpolation to approximate the spatial derivative of the dependent variable and a low order forward difference to approximate the time derivative of the dependent variable. Moreover, the stability of this method is studied. At last, the numerical results obtained by this way have been compared with the exact solution to show the efficiency of the method. The main advantage of the resulting scheme is that the algorithm is very simple, so it is very easy to implement.     

The Extended Alpha-triple I Algorithm Based on the Generalized Implication Operator

In this paper, the classical implication operator is generalized, and then the definition of generalized implication operator on [0, 1] is proposed. Based on generalized implication operator, the extended alpha-triple I principle is presented by generalizing the alpha-triple I algorithm. By analyzing the essence of alpha-triple Fuzzy Modus Ponens (FMP) and alpha-triple Fuzzy Modus Tollens (FMT) principles, and based on the generalized implication operator, the generalized calculating formula of extended alpha-triple I algorithm is proposed. At the same time, the reversibility on the proposed alpha-triple I algorithm is discussed. It is proved that Compositional Rule of Inference (CRI) is a special case of extended alpha-triple I algorithm. Finally, two implication operations are proposed to validate the proposed generalized equation.     

整数规划的广义填充函数算法

文[9,10]设计了直接求整数规划问题近似解的填充函数算法,但其所利用的文[2,3]的填充函数均带有参数,需要在算法过程中逐步调节。本文建立整数规划的广义填充函数的定义,说明了文[9,10]所利用的填充函数是整数规划问题的广义填充函数,并构造了一类不带参数的广义填充函数。进而本文设计了整数规划的一类不带参数的广义填充函数算法,数值试验表明算法是有效的。     

Finite termination of a Newton-type algorithm for a class of affine variational inequality problems

Many optimization problems can be reformulated as a system of equations. One may use the generalized Newton method or the smoothing Newton method to solve the reformulated equations so that a solution of the original problem can be found. Such methods have been powerful tools to solve many optimization problems in the literature. In this paper, we propose a Newton-type algorithm for solving a class of monotone affine variational inequality problems (AVIPs for short). In the proposed algorithm, the techniques based on both the generalized Newton method and the smoothing Newton method are used. In particular, we show that the algorithm can find an exact solution of the AVIP in a finite number of iterations under an assumption that the solution set of the AVIP is nonempty. Preliminary numerical results are reported.     

A mathematical analysis of Miller's algorithm

Summary The backward recurrence algorithm of J. C. P. Miller is generalized for homogeneous systems of linear difference equations. Necessary and sufficient conditions for both convergence and stability are given, and the results are demonstrated for scalar equations of arbitrary order. It is shown that the algorithm need not converge to a minimal solution. An application to the calculation of Incomplete Bessel functions is presented.     

Optimal ordering and pricing policy with supplier quantity discounts and price-dependent stochastic demand

We study a joint ordering and pricing problem for a retailer whose supplier provides all-unit quantity discount for the product. Both generalized disjunctive programming model and mixed integer nonlinear programming model are presented to formulate the problem. Some properties of the problem are analysed, based on which a solution algorithm is developed. Two numerical examples are presented to illustrate the problem, which are solved by our solution algorithm. Managerial analysis indicates that supplier quantity discount has much influence on the ordering and pricing policy of the retailer and more profit can be obtained when the supplier provides quantity discount.     

含H-单调映象的完全广义非线性变分包含

引入和研究了一类新的完全广义非线性变分包含.在Hilbert空间中利用与H-单调映象相联系的预解算子的性质,对完全广义非线性变分包含建立了解的存在性定理和构造了一种新的迭代算法,证明了由此算法生成的迭代序列强收敛于精确解.其算法和结果是最近文献中相应算法和结果的改进和推广.     

A multi-scale image reconstruction algorithm for electrical capacitance tomography

Electrical capacitance tomography (ECT) is considered as a promising process tomography (PT) technology, and its successful applications depend mainly on the precision and speed of the image reconstruction algorithms. In this paper, based on the wavelet multi-scale analysis method, an efficient image reconstruction algorithm is presented. The original inverse problem is decomposed into a sequence of inverse problems, which are solved successively from the largest scale to the smallest scale. At different scales, the inverse problem is solved by a generalized regularized total least squares (TLS) method, which is developed using a combinational minimax estimation method and an extended stabilizing functional, until the solution of the original inverse problem is found. The homotopy algorithm is employed to solve the objective functional. The proposed algorithm is tested by the noise-free capacitance data and the noise-contaminated capacitance data, and excellent numerical performances and satisfactory results are observed. In the cases considered in this paper, the reconstruction results show remarkable improvement in the accuracy. The spatial resolution of the reconstructed images by the proposed algorithm is enhanced and the artifacts in the reconstructed images can be eliminated effectively. As a result, a promising algorithm is introduced for ECT image reconstruction.     

Hilbert空间中关于松弛协强制映射的一类新的广义变分不等式组

在本文中,我们首先引入了一类新的关于松弛协强制映射的广义变分不等式组,通过用度量投影的方法,我们证明了这类广义变分不等式组解的存在性和唯一性.我们也建立了一类新的算法来逼近这一不等式组的解,本文的结果使得近期相关结果成为本文的特殊情况.     

Strongly sub-feasible direction method for constrained optimization problems with nonsmooth objective functions

In this paper, we propose a strongly sub-feasible direction method for the solution of inequality constrained optimization problems whose objective functions are not necessarily differentiable. The algorithm combines the subgradient aggregation technique with the ideas of generalized cutting plane method and of strongly sub-feasible direction method, and as results a new search direction finding subproblem and a new line search strategy are presented. The algorithm can not only accept infeasible starting points but also preserve the “strong sub-feasibility” of the current iteration without unduly increasing the objective value. Moreover, once a feasible iterate occurs, it becomes automatically a feasible descent algorithm. Global convergence is proved, and some preliminary numerical results show that the proposed algorithm is efficient.     

广义强非线性拟补问题*

利用本文中的算法,我们证明了广义强非线性拟补问题解的存在性及由算法产生的迭代序列的收敛性,改进和发展了Noor,Chang-Huang等人的结果.此外,也给出了求广义强非线性拟补问题的近似解的另一更一般的迭代算法并证明了由此迭代格式获得的近似解收敛于此补问题的精确解.     

广义非线性变分包含的带误差的近似点算法

引入和研究了一类新的广义非线性变分包含．在Hilbert空间中利用与极大η-单调映象相联系的预解算子的性质，对新的广义非线性变分包含建立了一个新的寻求近似解的带误差的近似点算法，并证明了求近似解序列强收敛于精确解．其所得结果是近期相关结果的改进和推广．     

广义几何规划的压缩共轭梯度路径非单调信赖域算法(英文)

In this paper,on the basis of making full use of the characteristics of unconstrained generalized geometric programming（GGP）,we establish a nonmonotonic trust region algorithm via the conjugate path for solving unconstrained GGP problem.A new type of condensation problem is presented,then a particular conjugate path is constructed for the problem,along which we get the approximate solution of the problem by nonmonotonic trust region algorithm,and further prove that the algorithm has global convergence and quadratic convergence properties.