Improved algorithm for mixed-integer quadratic programs and a computational study

In a recent paper [6] we suggested an algorithm for solving complicated mixed-integer quadratic programs, based on an equivalent formulation that employs a nonsingular transformation of variables. The objectives of the present paper are two. First, to present an improved version of this algorithm, which reduces substantially its computational requirements; second, to report on the results of a computational study with the revised algorithm.     

On solving a primal geometric program by partial dual optimization

The several published methods for mapping a dual solution estimate to a primal solution estimate in posynomial geometric programming provide no criteria for deciding how much deviation from primal feasibility, or discrepancy between the primal and dual objective function values, should be permitted before the primal solution estimate is accepted by the designer. This paper presents a new and simple dual-to-primal conversion method that uses the cost coefficients to provide a sound economic criterion for determining when to accept a primal solution estimate. The primal solution estimate generated is the exact solution to a modified primal obtained from the given primal by modifying the cost coefficients, with the exponent matrix left unchanged. The method is shown to have desirable properties when coupled with a convergent dual algorithm.     

Optimal design of a membrane separation process using signomial programming

A multistage membrane separation process for hydrogen recovery is described and formulated as a signomial programming problem. Two different configurations are examined. A 3-stage and a 5-stage process design problem are solved. The optimal solution to these programs is computed from an initial point that does not satisfy the mass balance or transport constraints of the process, using a primal-based geometric programming code. Also examined is the sensitivity of the optimal solution to changes in purity requirements. In all cases, computation times are very reasonable ranging from 2 to 4 seconds of IBM 370/165 CPU time.     

A greedy algorithm for solving a class of convex programming problems and its connection with polymatroid theory

We present a greedy algorithm for solving a special class of convex programming problems and establish a connection with polymatroid theory which yields a theoretical explanation and verification of the algorithm via some recent results of S. Fujishige.     

关于FLP问题一种新的单纯形算法的推广

指出FLP问题的一种新的单纯形算法[1]中主要结论成立的适用条件,并给出了该适用条件不成立时,一般条件下的推广.     

Optimal Channel Selection algorithm for CRahNs

As the data traffic is increasing, the spectrum bands are getting congested. It causes low latency and unreliable communication. Additional spectrum can be utilized to solve this problem but moving towards higher frequency means higher power requirement and increased cost. Cognitive radio network is another solution to this problem. It helps the nodes of a network to use the channels of the nearby bands which are not being used at that time. However, it has several challenges. One of these challenges is the transmission collision with the primary users of the network. Researchers have been working on this problem. However, it is still a major concern for the researchers. This paper proposes an algorithm that selects the optimal cognitive channel for the data transmission by the secondary user in such a way so that the transmission collision with the PU is minimized. After comparison with the existing latest similar protocol, the proposed protocol has shown 5.6% improvement in the throughput, 5.3% improvement in PDR. The delay is decreased by 0.6% and the transmission collision with PUs is reduced by 2.5%.     

求解线性不等式约束优化问题的一类信赖域算法

本文对线性不等式约束的非线性规划问题提出了一类信赖域算法,证明了算法所产生的序列的任一聚点为Ｋｕｈｎ－Ｔｕｃｋｅｒ点,并讨论了子问题求解的有效集方法．