求解单位L∞范数下带值约束的最大权完美匹配逆问题的算法

通过研究单位L_∞范数下的带值约束的最大权完美匹配逆问题的性质,将单位L_∞范数下最大权完美匹配逆问题转化为求解最大平均交替圈问题,给出一个求解该类问题的一个强多项式时间算法,其时间复杂度为O(n⁴).并通过一个算例,验证了给出的算法的有效性.     

问题1|d_j=d|Σw_jT_j的一个全多项式近似方案

本文对具有相同工期的单机最小化加权总误工问题进行了讨论.利用强NP-困难问题1ΣwjTj的一个O(n2)时间的近似算法,把该算法得到的目标值作为问题1|dj=d|ΣwjTj的一个上界,对问题1|dj=d|ΣwjTj给出全多项式近似方案(FPTAS).已知问题1|dj=d|ΣwjTj是一般意义下的NP-困难问题,并且已经有人对该问题给出了拟多项式时间算法,本文对已有结果进行了扩充.     

工件间具有串并有向图约束的单机随机排序问题

研究机器发生随机故障的单机排序问题,其中工件间的优先约束为串并有向图,目标函数为极小化加权完工时间和,证明了此问题多项式时间可解,并给出了多项式时间算法.     

点带约束成本的最短路问题

本文提出了点带约束成本的最短路问题,证明了该问题是ＮＰ－完全的,并利用动态规划给出了一个伪多项式算法,对所有顶点约束成本相同的情况,给出了一个时间复杂性为Ｏ（ｍｎ＾２）的算法,对最小点成本最短路问题,给出了一个时间复杂性为Ｏ（ｎ＾２）的算法。     

带点弧约束的最短路问题：复杂性和算法

本文首先提出了带点弧约束的最短路问题，证明了该问题属于ＮＰ－Ｃ，然后给出了一个伪多项式时间算法．最后给出了最小成本最短路问题的一个时间复杂性为Ｏ（ｎ２）的算法．     

具有时间与位置相关的两类平行机排序问题

研究带有维修时间限制的时间和位置效应平行机排序问题,涉及同型机和非同类机两种机器类型.工件的实际加工时间同时受到位置效应和时间效应影响,且机器具有维修限制.目标函数由机器负载,总完工时间与总等待时间组成.非同类机情形下,通过将排序问题转化为指派问题,给出多项式时间算法,其算法的时间复杂度为O(n~(k+2))/((k-1)!).同型机情形下通过转化目标函数,使用匹配算法得出排序问题的多项式时间解,其时间复杂度为O((2n+m+n log n)n~(k-1))/((k-1)!).     

有理单变元表示在优化问题上的应用

利用零维多项式系统的有理单变元表示,给出了求多项式在有限点集上的正性判定算法.同时,结合不等式证明,呈现了目标函数在零维系统约束下最优化的一个纯代数算法,从而将多元函数约束优化问题转化为单变元函数在单变元多项式约束下的优化问题.新算法不仅能处理目标函数为多项式的最优化问题,而且还能处理目标函数为有理分式函数和根式函数的的最优化问题,并且给出了目标函数最优值的精确区间表示,使得能任意精度地逼近最优值.     

框式线性规划的多项式预估校正内点算法

本文研究带线性约束的框式线性规划问题,给出了一个预估校正内点算法,分析了该算法的多项式计算复杂性,并证明其迭代复杂度为Ο(nL).     

离散加工时间的可控排序问题

本文主要研究了离散加工时间的可控排序问题,目标函数是总压缩费用约束下极小化最大完工时间,对单机工件有不同到达时间以及同型机工件到达时间都相同这两个问题,我们设计了伪多项式时间的动态规划算法,并给出了相应的FPTAS算法．     

多重三角T阵与多元多项式的快速除法

我们在[1]中给出了求三角形T矩阵的逆和计算一元多项式除法的O(nlogn)算法,改进了这两个问题已有的工作量为O(nlog~2n)的快速算法。本文给出了多重三角T阵的乘积、求逆和多元多项式的快速除法等快速方法,推广了[1]和[2]的结果。为叙述简便,我们仅就二重上三角形T阵与二元多项式除法讨论。由此不难推广到一般情形。     

Parallel machine scheduling with machine availability and eligibility constraints

In this paper we consider the problem of scheduling n independent jobs on m identical machines incorporating machine availability and eligibility constraints while minimizing the makespan. Each machine is not continuously available at all times and each job can only be processed on specified machines. A network flow approach is used to formulate this scheduling problem into a series of maximum flow problems. We propose a polynomial time binary search algorithm to either verify the infeasibility of the problem or solve it optimally if a feasible schedule exists.     

Parallel-machine scheduling with non-simultaneous machine available time

We consider a problem of scheduling n independent jobs on m parallel identical machines. The jobs are available at time zero, but the machines may not be available simultaneously at time zero. We concentrate on two goals separately, namely, minimizing a cost function containing total completion time and total absolute differences in completion times; minimizing a cost function containing total waiting time and total absolute differences in waiting times. In this paper, we present polynomial time algorithm to solve this problem.     

Exponential behaviour of the Butkovi?-Zimmermann algorithm for solving two-sided linear systems in max-algebra

In [P. Butkovi?, K. Zimmermann, A strongly polynomial algorithm for solving two-sided linear systems in max-algebra, Discrete Applied Mathematics 154 (3) (2006) 437-446] an ingenious algorithm for solving systems of two-sided linear equations in max-algebra was given and claimed to be strongly polynomial. However, in this note we give a sequence of examples showing exponential behaviour of the algorithm. We conclude that the problem of finding a strongly polynomial algorithm is still open.     

The integral stable allocation problem on graphs

As a generalisation of the stable matching problem Baïou and Balinski (2002) [1] defined the stable allocation problem for bipartite graphs, where both the edges and the vertices may have capacities. They constructed a so-called inductive algorithm, that always finds a stable allocation in strongly polynomial time. Here, we generalise their algorithm for non-bipartite graphs with integral capacities. We show that the algorithm does not remain polynomial, although we also present a scaling technique that makes the algorithm weakly polynomial.     

A capacity-rounding algorithm for the minimum-cost circulation problem: A dual framework of the Tardos algorithm

Recently, É. Tardos gave a strongly polynomial algorithm for the minimum-cost circulation problem and solved the open problem posed in 1972 by J. Edmonds and R.M. Karp. Her algorithm runs in O(m ² T(m, n) logm) time, wherem is the number of arcs,n is the number of vertices, andT(m, n) is the time required for solving a maximum flow problem in a network withm arcs andn vertices. In the present paper, taking an approach that is a dual of Tardos's, we also give a strongly polynomial algorithm for the minimum-cost circulation problem. Our algorithm runs in O(m ² S(m, n) logm) time and reduces the computational complexity, whereS(m, n) is the time required for solving a shortest path problem with a fixed origin in a network withm arcs,n vertices, and a nonnegative arc length function. The complexity is the same as that of Orlin's algorithm, recently developed by efficiently implementing the Edmonds-Karp scaling algorithm.     

A primal simplex algorithm that solves the maximum flow problem in at mostnm pivots and O(n 2 m) time

We propose a primal network simplex algorithm for solving the maximum flow problem which chooses as the arc to enter the basis one that isclosest to the source node from amongst all possible candidates. We prove that this algorithm requires at mostnm pivots to solve a problem withn nodes andm arcs, and give implementations of it which run in O(n ² m) time. Our algorithm is, as far as we know, the first strongly polynomial primal simplex algorithm for solving the maximum flow problem.This research was supported in part by NSF Grants DMS 85-12277 and CDR 84-21402 and ONR Contract N00014-87-K0214.     

A faster capacity scaling algorithm for minimum cost submodular flow

We describe an O(n ⁴ hmin{logU,n ²logn}) capacity scaling algorithm for the minimum cost submodular flow problem. Our algorithm modifies and extends the Edmonds–Karp capacity scaling algorithm for minimum cost flow to solve the minimum cost submodular flow problem. The modification entails scaling a relaxation parameter δ. Capacities are relaxed by attaching a complete directed graph with uniform arc capacity δ in each scaling phase. We then modify a feasible submodular flow by relaxing the submodular constraints, so that complementary slackness is satisfied. This creates discrepancies between the boundary of the flow and the base polyhedron of a relaxed submodular function. To reduce these discrepancies, we use a variant of the successive shortest path algorithm that augments flow along minimum cost paths of residual capacity at least δ. The shortest augmenting path subroutine we use is a variant of Dijkstra’s algorithm modified to handle exchange capacity arcs efficiently. The result is a weakly polynomial time algorithm whose running time is better than any existing submodular flow algorithm when U is small and C is big. We also show how to use maximum mean cuts to make the algorithm strongly polynomial. The resulting algorithm is the first capacity scaling algorithm to match the current best strongly polynomial bound for submodular flow. Received: August 6, 1999 / Accepted: July 2001?Published online October 2, 2001     

The MA-ordering max-flow algorithm is not strongly polynomial for directed networks

Quite recently, Fujishige (Oper. Res. Lett. 31 (2003) 176-178) developed a weakly polynomial-time algorithm for the maximum flow problem by applying the maximum adjacency (MA) ordering technique to directed networks. In this note, we show that the algorithm is not strongly polynomial by giving a real-valued instance for which the algorithm does not terminate.     

有预算限制的最大多种物资流问题

研究有预算限制的最大多种物资流问题,给出了这个问题的不依赖物资数k的全多项式时间近似算法,其算法复杂性是O~(-ε2m2).同时,利用有预算限制的最大多种物资流问题的研究结果,我们也得到了费用最小的最大多种物资流问题的近似算法和算法复杂性.     

具有多个不可用时间段的单机供应链排序问题

在单机供应链排序问题中, 机器会有多个长度确定的不可用时间段,它仅可以在可用时间段内加工工件,且每个可用时间段的长度不大于给定的常数.多个完工工件可组成一批由一个容量无限制的运输工具发送给客户.问题的目标是如何 安排工件的加工、发送以及不可用时间段,以使总发送时间与总发送费用之和达到最小. 对于工件加工可恢复的情况,可在多项式时间 O(n^2) 内得到最优序. 对于工件加工不可恢复的情况,证明了问题是强NP-难的, 并提出了~2-近似算法.