Repeated Price Search

Consumers check few sites in online purchases. Previous research and experiments we perform demonstrate that consumers can not calculate the optimal strategy for price search. They use heuristics whose performance is better than random and less than optimal. To investigate online price search performance we survey student online textbook purchases. Students achieve good performance because they start with a good strategy and online market organization of marketplace and meta-search sites. An important factor is that algorithms at sites searched perform calculations that reduce the computational complexity of the search.     

A Search Algorithm for Calculating Validated Reliability Bounds

The search algorithm presented allows the CDF of a dependent variable to be bounded with 100% confidence, and allows for a guaranteed evaluation of the error involved. These reliability bounds are often enough to make decisions, and often require a minimal number of function evaluations. The procedure is not intrusive, i.e. it can be equally applied when the function is a complex computer model (black box). The proposed procedure can handle input information consisting of probabilistic, interval-valued, set-valued, or random-set-valued information, as well as any combination thereof. The function as well as the joint pdf of the input variables can be of any type.     

Improved Bounds for Finger Search on a RAM

We present a new finger search tree with O(loglogd) expected search time in the Random Access Machine (RAM) model of computation for a large class of input distributions. The parameter d represents the number of elements (distance) between the search element and an element pointed to by a finger, in a finger search tree that stores n elements. Our data structure improves upon a previous result by Andersson and Mattsson that exhibits expected O(loglogn) search time by incorporating the distance d into the search time complexity, and thus removing the dependence on n. We are also able to show that the search time is O(loglogd+?(n)) with high probability, where ?(n) is any slowly growing function of n. For the need of the analysis we model the updates by a “balls and bins” combinatorial game that is interesting in its own right as it involves insertions and deletions of balls according to an unknown distribution.     

面向直流电机模型时变参数的在线辨识方法

在直流电机建模优化问题的研究中,由于直流电机的时变参数随现场工况的变化而变化,导致采用离线辨识方法无法估计直流电机的系统模型,针对直流电机系统参数的时变性,提出一种直流电机时变参数的在线辨识策略.依据直流电机的电势平衡方程和转矩平衡方程,得到直流电机的传递函数,转化为电动机角速度和电压之间的差分方程,然后,采用递推最小二乘算法进行在线辨识,从而实现直流电机系统模型在线辨识的目的.搭建直流电机在线辨识系统实验平台,采用改变直流电机载荷的方式,进行了在线实时辨识的实验,实验结果表明:改进方法辨识效果良好,较好地实现了直流电机在参数时变的情况下,辨识效果良好,辨识模型较好地跟踪系统参数的变化.     

一种新的时变大纯滞后对象在线辨识方法

对具有时变特性的大纯滞后对象进行参数估计是系统辨识的一项重要课题,该文结合递归遗忘最小二乘法和互相关函数,提出了一种联合辨识算法,该方法扩展了递归遗忘最小二乘算法的应用范围。其中,递归遗忘最小二乘法用于快速地辨识对象的动态参数,而对输入输出信号进行滤波后,对象的滞后时间参数的辨识由互相关函数实现。仿真结果表明,此种方法对于时变大纯滞后对象的在线辨识是有效的,并且优于间接估计法;该文的方法具有算法简便,实用性强的特点。     

Optimal Search and One-Way Trading Online Algorithms

This paper is concerned with the time series search and one-way trading problems. In the (time series) search problem a player is searching for the maximum (or minimum) price in a sequence that unfolds sequentially, one price at a time. Once during this game the player can decide to accept the current price p in which case the game ends and the player's payoff is p . In the one-way trading problem a trader is given the task of trading dollars to yen. Each day, a new exchange rate is announced and the trader must decide how many dollars to convert to yen according to the current rate. The game ends when the trader trades his entire dollar wealth to yen and his payoff is the number of yen acquired. The search and one-way trading are intimately related. Any (deterministic or randomized) one-way trading algorithm can be viewed as a randomized search algorithm. Using the competitive ratio as a performance measure we determine the optimal competitive performance for several variants of these problems. In particular, we show that a simple threat-based strategy is optimal and we determine its competitive ratio which yields, for realistic values of the problem parameters, surprisingly low competitive ratios. We also consider and analyze a one-way trading game played against an adversary called Nature where the online player knows the probability distribution of the maximum exchange rate and that distribution has been chosen by Nature. Finally, we consider some applications for a special case of portfolio selection called two-way trading in which the trader may trade back and forth between cash and one asset. Received October 19, 1998; revised August 12, 1999.     

Practical Algorithms and Lower Bounds for Similarity Search in Massive Graphs

To exploit the similarity information hidden in the hyperlink structure of the Web, this paper introduces algorithms scalable to graphs with billions of vertices on a distributed architecture. The similarity of multistep neighborhoods of vertices are numerically evaluated by similarity functions including SimRank, a recursive refinement of cocitation, and PSimRank, a novel variant with better theoretical characteristics. Our methods are presented in a general framework of Monte Carlo similarity search algorithms that precompute an index database of random fingerprints, and at query time, similarities are estimated from the fingerprints. We justify our approximation method by asymptotic worst-case lower bounds: we show that there is a significant gap between exact and approximate approaches, and suggest that the exact computation, in general, is infeasible for large-scale inputs. We were the first to evaluate SimRank on real Web data. On the Stanford WebBase graph of 80M pages the quality of the methods increased significantly in each refinement step until step four     

Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization

Randomized search heuristics like local search, tabu search, simulated annealing, or all kinds of evolutionary algorithms have many applications. However, for most problems the best worst-case expected run times are achieved by more problem-specific algorithms. This raises the question about the limits of general randomized search heuristics. Here a framework called black-box optimization is developed. The essential issue is that the problem but not the problem instance is knownto the algorithm which can collect information about the instance only by asking for the value of points in the search space. All known randomized search heuristics fit into this scenario. Lower bounds on the black-box complexity of problems are derived without complexity theoretical assumptions and are compared with upper bounds in this scenario.     

Performance Analysis of Gradient Neural Network Exploited for Online Time-Varying Matrix Inversion

This technical note presents theoretical analysis and simulation results on the performance of a classic gradient neural network (GNN), which was designed originally for constant matrix inversion but is now exploited for time-varying matrix inversion. Compared to the constant matrix-inversion case, the gradient neural network inverting a time-varying matrix could only approximately approach its time-varying theoretical inverse, instead of converging exactly. In other words, the steady-state error between the GNN solution and the theoretical/exact inverse does not vanish to zero. In this technical note, the upper bound of such an error is estimated firstly. The global exponential convergence rate is then analyzed for such a Hopfield-type neural network when approaching the bound error. Computer-simulation results finally substantiate the performance analysis of this gradient neural network exploited to invert online time-varying matrices.     

Sampling-Based Tabu Search Approach for Online Path Planning

Abstract

Path planning in unknown environments is one of the most challenging research areas in robotics. In this class of path planning, the robot acquires the information from its sensory system. Sampling-based path planning is one of the famous approaches with low memory and computational requirements that has been studied by many researchers during the past few decades. We propose a sampling-based algorithm for path planning in unknown environments using Tabu search. The Tabu search component of our method guides the sampling to find the samples in the most promising areas and makes the sampling procedure more intelligent. The simulation results show the efficient performance of the proposed approach in different types of environments. We also compare the performance of the algorithm with some of the well-known path planning approaches, including Bug1, Bug2, SRT, potential fields and the visibility graph. Furthermore, two different sampling strategies were used in the sampling procedure, including uniform and Gaussian distributions.     

Absolute and Asymptotic Bounds for Online Frequency Allocation in Cellular Networks

Given a cellular (mobile telephone) network, whose geographical coverage area is divided into hexagonal cells, phone calls are serviced by assigning frequencies to them so that no two calls emanating from the same or neighboring cells are assigned the same frequency. Assuming an online arrival of calls, the goal is to minimize the span of frequencies used to serve all of the calls.     

Li-Function Activated Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality

Neural Processing Letters - In the previous work, a typical recurrent neural network termed Zhang neural network (ZNN) has been developed for various time-varying problems solving. Based on the...     

Detection of Breaking News from Online Web Search Queries

Many people enter queries to Google or Yahoo! in order to search useful information from the Web. Queries given to search engines can be regarded as the resources for detecting people’s information needs. It is often reported that many people perform search intensively after worldwide disasters or accidents. This paper describes a method for detecting such breaking news from search queries that are available online. In our method, real time search queries are obtained and filtered with news words extracted from a news site. Experimental results show that our method has abilities of detecting breaking news from more than 25 million search queries for six months.

面向在线空间信息的自动化搜索

近年来,越来越多的空间信息实现了在线发布和在线更新。但这些在线空间信息的分布广泛性和发展无序性导致了最终用户难以找到所需的在线空间信息,因此面向在线空间信息的自动化搜索已经成为空间信息共享的一个重要研究内容,其设计目标为自动地帮助用户寻找感兴趣的空间信息。论文从互联网上空间信息的存在形式和提供方式入手,分析了网络空间信息系统的一般结构;通过从搜索对象、搜索方法和用户界面等方面对通用Web信息搜索和面向空间信息搜索进行了比较,得出了实现在线空间信息的自动化搜索必须首先规范化网络空间信息系统的结论,最后总结了面向空间信息的搜索需要解决的困难并分析指出其研究的主要内容。     

Labeled Search Trees and Amortized Analysis: Improved Upper Bounds for NP-Hard Problems

A sequence of exact algorithms to solve the Vertex Cover and Maximum Independent Set problems have been proposed in the literature. All these algorithms appeal to a very conservative analysis that considers the size of the search tree, under a worst-case scenario, to derive an upper bound on the running time of the algorithm. In this paper we propose a different approach to analyze the size of the search tree. We use amortized analysis to show how simple algorithms, if analyzed properly, may perform much better than the upper bounds on their running time derived by considering only a worst-case scenario. This approach allows us to present a simple algorithm of running time O(1.194^kk² + n) for the parameterized Vertex Cover problem on degree-3 graphs, and a simple algorithm of running time O(1.1255ⁿ) for the Maximum Independent Set problem on degree-3 graphs. Both algorithms improve the previous best algorithms for the problems.     

基于多渠道供应链博弈的线上价格策略

对一个开辟线上直销渠道的制造商和一个双渠道零售商组成的供应链系统，考虑线下渠道促销努力及消费者搭便车行为，在零售商主导的Stackelberg博弈下，就线上价格是否一致，研究了各渠道的定价策略。结果表明：当制造商线上渠道基本市场份额较大时，对制造商和零售商来说，线上价格不一致是最优策略。当制造商线上渠道基本市场份额较小时，双方都倾向拥有线上价格的决定权；并且当制造商承担部分促销成本时，零售商能接受制造商决定线上价格。     

新型海淘购物比价引擎方案设计

海淘在经过最近几年的发展,已经成为很多中国消费者的购物方式之一。除了商家选择、商品购买、商品转运外,消费者最看重的环节之一便是价格比对。但是,目前大多数比价引擎仅支持英文或中文表述的网站,极少可以把对应的跨境商品进行价格比较。因此,本文提出一种能进行国内外相同商品比价的方案,通过以美中两国Amazon购物网站进行比价搜索,实现更好的比价效果。系统会结合翻译模块,能够让用户用中文或英文进行商品搜索。在收到用户搜索请求之后,会优先审查本地数据库是否包含用户之前的搜索记录,如果有则反馈本地记录,如果没有则实时抓取美中两国Amazon相应商品信息。另外,虽然很多比价引擎能够识别中文搜索,但识别精准度和范围都不是很好。为了提高搜索精度,系统将网上翻译API和本地电商词汇中英对照表结合,对用户输入的中英文进行实时翻译。为减少商业利益影响,不同于Amazon原始的商品排序,系统在Amazon原始排序算法基础上,对排序做了改进来实现关键字的排序优化。     

Improved Lower Bounds on the Price of Stability of Undirected Network Design Games

Bounding the price of stability of undirected network design games with fair cost allocation is a challenging open problem in the Algorithmic Game Theory research agenda. Even though the generalization of such games in directed networks is well understood in terms of the price of stability (it is exactly H _n , the n-th harmonic number, for games with n players), far less is known for network design games in undirected networks. The upper bound carries over to this case as well while the best known lower bound is 42/23≈1.826. For more restricted but interesting variants of such games such as broadcast and multicast games, sublogarithmic upper bounds are known while the best known lower bound is 12/7≈1.714. In the current paper, we improve the lower bounds as follows. We break the psychological barrier of 2 by showing that the price of stability of undirected network design games is at least 348/155≈2.245. Our proof uses a recursive construction of a network design game with a simple gadget as the main building block. For broadcast and multicast games, we present new lower bounds of 20/11≈1.818 and 1.862, respectively.     

搜索引擎的一种在线中文查询纠错方法

该文主要解决中文搜索引擎的查询纠错问题。错误的查询,已经偏离用户真实的搜索意图时,搜索质量很差,甚至导致搜索结果数为零。为此该文提出了一种服务于实际搜索引擎,较为完整的查询纠错方案。该文重点描述了纠错查询候选生成、纠错查询候选评价、以及基于核函数,挑选最优纠错查询候选等内容。通过在开放测试集上的准确率/召回率验证,以及在搜索引擎中实际的DCG评测,该文的方案都取得了较好的效果。     

Expected Optimal Feedback with Time-Varying Parameters

In this paper we derive the closed loop form of the Expected Optimal Feedback rule, sometimes called passive learning stochastic control, with time varying parameters. As such this paper extends the work of Kendrick (Stochastic control for economic models, 1981; Stochastic control for economic models, 2002, Chap. 6) where parameters are assumed to vary randomly around a known constant mean. Furthermore, we show that the cautionary myopic rule in Beck and Wieland (J Econ Dyn Control 26:1359–1377, 2002) model, a test bed for comparing various stochastic optimizations approaches, can be cast into this framework and can be treated as a special case of this solution.