Polynomial algorithm of limited propositional deduction

For the problem of propositional satisfiability a polynomial algorithm of limited propositional deduction is proposed which can be viewed as a sort of boolean constraint propagation mechanism. It can be embodied in a backtracking search program for propositional satisfiability problems to make search efficient. The efficiency is gained in two ways:One is to use the algorithm to derive literals so as to overcome the ambiguities in search. The other is to exploit the consequence sets of unbound atoms generated during limited deduction as a heuristic measure for possible choices. The experiments have shown remarkable improvement in reducing search space.

SIMULATED ANNEALING BASED POLYNOMIAL TIME QOS ROUTING ALGORITHM FOR MANETS

Multi-constrained Quality-of-Service (QoS) routing is a big challenge for Mobile Ad hoc Networks (MANETs) where the topology may change constantly. In this paper a novel QoS Routing Algorithm based on Simulated Annealing (SA_RA) is proposed. This algorithm first uses an energy function to translate multiple QoS weights into a single mixed metric and then seeks to find a feasible path by simulated annealing. The pa- per outlines simulated annealing algorithm and analyzes the problems met when we apply it to Qos Routing (QoSR) in MANETs. Theoretical analysis and experiment results demonstrate that the proposed method is an effective approximation algorithms showing better performance than the other pertinent algorithm in seeking the (approximate) optimal configuration within a period of polynomial time.

A Note on DP Algorithm for Batching Scheduling to Minimize Maximum Lateness

In parallel-batching machine scheduling, all jobs in a batch start and complete at the same time, and the processing time of the batch is the maximum processing time of any job in it. For the unbounded parallel-batching machine scheduling problem of minimizing the maximum lateness, denoted 1|p-batch|L_(max), a dynamic programming algorithm with time complexity O(n^2) is well known in the literature.Later, this algorithm is improved to be an O(n log n) algorithm. In this note, we present another O(n log n) algorithm with simplifications on data structure and implementation details.

Solving the Binary Linear Programming Model in Polynomial Time

The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programming problem is transformed into a convex quadratic programming problem. The convex quadratic programming problem is then solved by interior point algorithms. This settles one of the open problems of whether P = NP or not. The worst case complexity of interior point algorithms for the convex quadratic problem is polynomial. It can also be shown that every liner integer problem can be converted into binary linear problem.

ON THE BOTTLENECK CAPACITY EXPANSION PROBLEMS ON NETWORKS

This article considers a class of bottleneck capacity expansion problems. Such problems aim to enhance bottleneck capacity to a certain level with minimum cost. Given a network G（V,A,C^-） consisting of a set of nodes V = {v1,v2,... ,vn}, a set of arcs A C {（vi,vj） ｜ i = 1,2,...,n; j = 1,2,...,n} and a capacity vector C. The component C^-ij of C is the capacity of arc （vi, vj）. Define the capacity of a subset A′ of A as the minimum capacity of the arcs in A, the capacity of a family F of subsets of A is the maximum capacity of its members. There are two types of expanding models. In the arc-expanding model, the unit cost to increase the capacity of arc （vi, vj） is ωij. In the node-expanding model, it is assumed that the capacities of all arcs （vi, vj） which start at the same node vi should be increased by the same amount and that the unit cost to make such expansion is wi. This article considers three kinds of bottleneck capacity expansion problems （path, spanning arborescence and maximum flow） in both expanding models. For each kind of expansion problems, this article discusses the characteristics of the problems and presents several results on the complexity of the problems.

AN INVERSE MAXIMUM CAPACITY PATH PROBLEM WITH LOWER BOUND CONSTRAINTS

The computational complexity of inverse mimimum capacity path problem with lower bound on capacity of maximum capacity path is examined, and it is proved that solution of this problem is NP-complete. A strong polynomial algorithm for a local optimal solution is provided.

ROBUST PARTIAL INVERSE NETWORK FLOW PROBLEMS

In this paper,a new model for inverse network flow problems,robust partial inverse problem is presented. For a given partial solution,the robust partial inverse problem is to modify the coefficients optimally such that all full solutions containing the partial solution become optimal under new coefficients. It has been shown that the robust partial inverse spanning tree problem can be formulated as a combinatorial linear program,while the robust partial inverse minimum cut problem and the robust partial inverse assignment problem can be solved by combinatorial strongly polynomial algorithms.

A Parallel Algorithm for Finding Roots of a Complex Polynomial

A distribution theory of the roots of a polynomial and a parallel algorithm for finding roots of a complex polynomial based on that theory are developed in this paper. With high parallelism, the algorithm is an im- provement over the Wilf algorithm.

ON THE COMPLEXITY OF A PL HOMOTOPY ALGORITHM FOR ZEROS OF POLYNOMIALS

A PL homotopy algorithm is modified to yield a polynomial-time result on its computational complexity.We prove that the cost of locating all zeros of a polynomial of degree n to an accuracy of ε(measured by the number of evaluations of the polynomial)grows no faster than O(max{n^4,n^3log_2(n/ε)}).This work is in response to a question raised in a paper by S.Smale as to the efficiency of piecewise linear methods in solving equations.In comparison with a few results reported,the algorithm under discussion is the only one providing correct multiplicities and the only one employing vector labelling.

SINGLE MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES AND COMPRESSION COSTS （PART I：EQUAL TIMES AND COSTS）

Abstract Most papers in scheduling research have treated individual job processing times as fixed parameters. However, in many practical situations, a manager may control processing time by reallocating resources. In this paper, authors consider a machine scheduling problem with controllable processing times. In the first part of this paper, a special case where the processing times and compression costs are uniform among jobs is discussed. Theoretical results are derived that aid in developing an O(n 2) algorithm to slove the problem optimally. In the second part of this paper, authors generalize the discussion to general case. An effective heuristic to the general problem will be presented.

SINGLE MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES AND COMPRESSION COSTS ： PROOF OF THEOREMS

Abstract This report is virtually the appendix part of the author's previous paper which includes the proofs for the theorems and lemmas.

An Interior-Point Algorithm for Linear Programming with Optimal Selection of Centering Parameter and Step Size

For interior-point algorithms in linear programming,it is well known that the selection of the centering parameter is crucial for proving polynomiality in theory and for efficiency in practice.However,the selection of the centering parameter is usually by heuristics and separated from the selection of the line-search step size.The heuristics are quite different while developing practically efficient algorithms,such as Mehrotra’s predictor–corrector(MPC)algorithm,and theoretically efficient algorithms,such as short-step path-following algorithm.This introduces a dilemma that some algorithms with the best-known polynomial bound are least efficient in practice,and some most efficient algorithms may not be convergent in polynomial time.Therefore,in this paper,we propose a systematic way to optimally select the centering parameter and linesearch step size at the same time,and we show that the algorithm based on this strategy has the best-known polynomial bound and may be very efficient in computation for real problems.

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.

Inverse Maximum Flow Problem Under the Combination of the Weighted l_(2)Norm and the Weighted Hamming Distance

The idea of the inverse optimization problem is to adjust the values of the parameters so that the observed feasible solutions are indeed optimal.The modification cost is measured by different norms,such asl1,l2,l∞norms and the Hamming distance,and the goal is to adjust the parameters as little as possible.In this paper,we consider the inverse maximum flow problem under the combination of the weighted l2 norm and the weighted Hamming distance,i.e.,the modification cost is fixed in a given interval and depends on the modification out of the given interval.We present a combinatorial algorithm which can be finished in O(nm)to solve it due to the minimum cut of the residual network.

On Scheduling a Deteriorating Rate-Modifying Activity to Minimize the Number of Tardy Jobs

We investigate a single-machine scheduling problem,where a deteriorating rate-modifying activity can be performed on the machine to reduce the processing times of jobs.The objective is to minimize the number of tardy jobs.Under the assumption that the duration of the rate-modifying activity is a nonnegative and nondecreasing function on its starting time,we propose an optimal polynomial time algorithm running in O(n^(3))time.