The Duo-Item Bisection Auction

This paper proposes an iterative sealed-bid auction for selling multiple heterogeneous items to bidders interested in buying at most one item. It generalizes the single item bisection auction (Grigorieva et al. Econ Theory, 30:107–118, 2007) to the environment with multiple heterogeneous items. We focus on the case with two items for sale. We show that the auction elicits a minimal amount of information on preferences required to find the Vickrey–Clark–Groves outcome (Clarke, Public Choice, XI:17–33, 1971; Groves, Econometrica, 61:617–631, 1973; Vickrey, J Finance, 16:8–37, 1961), when there are two items for sale and an arbitrary number of bidders.     

Constraint-Based Winner Determination for Auction-Based Scheduling

This paper presents a formulation and an algorithm for the winner determination problem in auction-based scheduling. Without imposing a time line discretization, the proposed approach allows bidders to bid for the processing of a set of jobs using a requirement-based bidding language, which naturally represents scheduling constraints. The proposed winner determination algorithm uses a depth first branch and bound search. The search branches on bids, and a constraint-directed scheduling procedure is used at each node to verify the feasibility of the temporary schedule. Experiments show that the proposed algorithm is on average more than an order of magnitude faster than a commercial optimization package, CPLEX 10.0.     

Approximations to auctions of digital goods with share-averse bidders

We consider the case of a digital product for share-averse bidders, where the product can be sold to multiple buyers who experience some disutility from other firms or consumers owning the same product. We model the problem of selling a digital product to share-averse bidders as an auction and apply a Bayesian optimal mechanism design. We also design constant-approximation algorithms in the prior-free setting including both average- and worst-case analyses.     

A fuzzy TOPSIS based approach for e-sourcing

This study presents a methodology for solving the sealed bid, multi-attribute reverse auction problem of e-sourcing in which the sales item is defined by several attributes, the buyer is auctioneer, and the suppliers are the bidders. There is only one buyer and a number of suppliers. Both qualitative and quantitative attributes of benefit and cost types are considered for solving the winner determination (WD) problem of reverse auction. Here, the WD problem is considered as multi-criteria decision making problem (MCDM). In order to address the imprecision of suppliers or decision makers in formulating the preference value of various attributes in MCDM, a fuzzy TOPSIS based methodology along with a mechanism for determination of fuzzy linguistic value of each attribute is proposed in this article. Entropy method is utilised to enumerate the weights of various attributes automatically without involvement of decision makers. An illustrative example is presented to demonstrate the applicability of the proposed methodology.     

On certain connectivity properties of the internet topology

We show that random graphs in the preferential connectivity model have constant conductance, and hence have worst-case routing congestion that scales logarithmically with the number of nodes. Another immediate implication is constant spectral gap between the first and second eigenvalues of the random walk matrix associated with these graphs. We also show that the expected frugality (overpayment in the Vickrey–Clarke–Groves mechanism for shortest paths) of a sparse Erdős–Renyi random graph is bounded by a small constant.     

获胜者确定问题的建模与启发式算法

获胜者确定问题是组合拍卖机制的核心问题．因此,对基于OR与XOR标集的获胜者确定问题建立了0-1规划模型,并且提出了免疫算子与单亲算子相结合的启发式算法．提出多个启发式规则以扩大标比较范围,并应用在预处理中缩减解空间．设计了多个评价函数评估标的优劣,从而将特征知识引入到免疫算子中．仿真实验表明,对大规模问题的求解具有良好的寻优效率和求解质量,免疫算子对达优率和收敛速度都有着明显的提升作用．     

Faster algorithms for mean-payoff games

In this paper, we study algorithmic problems for quantitative models that are motivated by the applications in modeling embedded systems. We consider two-player games played on a weighted graph with mean-payoff objective and with energy constraints. We present a new pseudopolynomial algorithm for solving such games, improving the best known worst-case complexity for pseudopolynomial mean-payoff algorithms. Our algorithm can also be combined with the procedure by Andersson and Vorobyov to obtain a randomized algorithm with currently the best expected time complexity. The proposed solution relies on a simple fixpoint iteration to solve the log-space equivalent problem of deciding the winner of energy games. Our results imply also that energy games and mean-payoff games can be reduced to safety games in pseudopolynomial time.     

An optimal multiprocessor combinatorial auction solver

A combinatorial auction (CA) is an auction that permits bidders to bid on bundles of goods rather than just a single item. Unfortunately, winner determination for CAs is known to be NP-hard. In this paper, we propose a distributed algorithm to compute optimal solutions to this problem. The algorithm uses nagging, a technique for parallelizing search in heterogeneous distributed computing environments. Here, we show how nagging can be used to parallelize a branch-and-bound algorithm for this problem, and provide empirical results supporting both the performance advantage of nagging over more traditional partitioning methods as well as the superior scalability of nagging to larger numbers of processors.     

Relative loss bounds for single neurons

We analyze and compare the well-known gradient descent algorithm and the more recent exponentiated gradient algorithm for training a single neuron with an arbitrary transfer function. Both algorithms are easily generalized to larger neural networks, and the generalization of gradient descent is the standard backpropagation algorithm. We prove worst-case loss bounds for both algorithms in the single neuron case. Since local minima make it difficult to prove worst case bounds for gradient-based algorithms, we must use a loss function that prevents the formation of spurious local minima. We define such a matching loss function for any strictly increasing differentiable transfer function and prove worst-case loss bounds for any such transfer function and its corresponding matching loss. The different forms of the two algorithms' bounds indicates that exponentiated gradient outperforms gradient descent when the inputs contain a large number of irrelevant components. Simulations on synthetic data confirm these analytical results.     

Iterative Dutch combinatorial auctions

The combinatorial auction problem can be modeled as a weighted set packing problem. Similarly the reverse combinatorial auction can be modeled as a weighted set covering problem. We use the set packing and set covering formulations to suggest novel iterative Dutch auction algorithms for combinatorial auction problems. We use generalized Vickrey auctions (GVA) with reserve prices in each iteration. We prove the convergence of the algorithms and show that the solutions obtained using the algorithms lie within provable worst case bounds. We conduct numerical experiments to show that in general the solutions obtained using these algorithms are much better than the theoretical bounds.     

Allocating fixed-priority periodic tasks on multiprocessor systems

In this paper, we study the problem of allocating a set of periodic tasks on a multiprocessor system such that tasks are scheduled to meet their deadlines on individual processors by the Rate-Monotonic scheduling algorithm. A new schedulability condition is developed for the Rate-Monotonic scheduling that allows us to develop more efficient on-line allocation algorithms. Two on-line allocation algorithms—RM-FF and RM-BF are presented, and shown that their worst-case performance, over the optimal allocation, is upper bounded by 2.33 and lower bounded by 2.28. Then RM-FF and RM-BF are further improved to form two new algorithms: Refined-RM-FF (RRM-FF) and Refined-RM-BF (RRM-BF), both of which have a worst-case performance bound of 2. We also show that when the maximum allowable utilization of a task is small, the worst-case performance of all the new algorithms can be significantly improved. The worst-case performance bounds of RRM-FF and RRM-BF are currently the best bounds in the class of on-line scheduling algorithms proposed to solve the same scheduling problem. Simulation studies show that the average-case performance of the newly proposed algorithms is significantly superior to those in the existing literature.     

一种新的安全电子拍卖协议

安全性是电子商务的一个前提。从保护投标者的角度出发,基于Lagrange插值法提出了一个完全匿名的安全电子拍卖协议。协议不仅能对除中标者外的其他所有投标者的投标价进行保密,还能有效地保护中标者的安全。而且,匿名机制也保证在整个电子拍卖过程中,无人可以操纵其他人的投标。同时,整个协议运行的通信代价很小,效率较高。     

多目标人工蜂群算法在服务组合优化中的应用

应用广泛的聚集函数法可将多目标问题转换为单目标问题,但函数设计困难,通用性不强,且优化结果不能直观反映各目标的优化情况。提出了一个基于Pareto占优的多目标人工蜂群算法,改进了邻域搜索策略,给出一个对比实验。实验结果表明,改进算法在个体多样性与对Pareto最优边界的趋近程度方面均有优势。     

Recognition is not parsing — SPPF-style parsing from cubic recognisers

In their recogniser forms, the Earley and RIGLR algorithms for testing whether a string can be derived from a grammar are worst-case cubic on general context free grammars (CFG). Earley gave an outline of a method for turning his recognisers into parsers, but it turns out that this method is incorrect. Tomita’s GLR parser returns a shared packed parse forest (SPPF) representation of all derivations of a given string from a given CFG but is worst-case unbounded polynomial order. The parser version of the RIGLR algorithm constructs Tomita-style SPPFs and thus is also worst-case unbounded polynomial order. We have given a modified worst-case cubic GLR algorithm, that, for any string and any CFG, returns a binarised SPPF representation of all possible derivations of a given string. In this paper we apply similar techniques to develop worst-case cubic Earley and RIGLR parsing algorithms.     

A Lower Bound for Nearly Minimal Adaptive and Hot Potato Algorithms

Recently, Chinn et al. [10] presented lower bounds for store-and-forward permutation routing algorithms on the n \times n mesh with bounded buffer size and where a packet must take a shortest (or minimal ) path to its destination. We extend their analysis to algorithms that are nearly minimal. We also apply this technique to the domain of hot potato algorithms, where there is no storage of packets and the shortest path to a destination is not assumed (and is in general impossible). We show that ``natural' variants and ``improvements' of several algorithms in the literature perform poorly in the worst case. As a result, we identify algorithmic features that are undesirable for worst-case hot potato permutation routing. Recent works in hot potato routing have tried to define simple and greedy classes of algorithms. We show that when an algorithm is too simple and too greedy, its performance in routing permutations is poor in the worst case. Specifically, the technique of [10] is also applicable to algorithms that do not necessarily send packets in minimal or even nearly minimal paths: it may be enough that they naively attempt to do so when possible. In particular, our results show that a certain class of greedy algorithms that was suggested recently by Ben-Dor et al. [6] contains algorithms that have poor performance in routing worst-case permutations. Received August 24, 1995; revised May 27, 1997.     

Parallel machine scheduling with multiple unloading servers

We study a parallel machine scheduling problem with multiple unloading servers. After a machine completes processing one job, an unloading server is needed to remove the job from the machine. Only after unloading, the machine is available for processing the next job. The model is motivated by the milk run operations of a logistics company that faces limited unloading docks at the warehouse. Our interest is to minimize the total completion time of the jobs. We show that the shortest-processing-time-first (SPT) algorithm has a worst-case bound of 2. We also develop other improved heuristic algorithms as well as a branch-and-bound algorithm to solve the problem. Computational experiments show that our algorithms are efficient and effective.     

Asymptotic properties of set membership identification algorithms

This paper addresses the asymptotic worst-case properties of set membership identification (SMID) algorithms. We first present a set membership identification algorithm which can be used with a model structure consisting of parametric and nonparametric uncertainty, as well as output additive disturbances. This algorithm is then studied in the context of asymptotic worst-case behavior. We derive lower bounds on the worst-case achievable identification error measured by the volume, as well as the sum-of-sidelengths of the identified ellipsoidal uncertainty sets. We then show that there exist inputs which can shrink the uncertainty sets to these lower bounds.     

梯度策略的多目标GANs帕累托最优解算法

针对基于梯度策略的多目标优化算法无法适用于多目标、高维度的生成对抗网络(Generative Adversarial Nets, GANs)及多目标GANs中利用交叉验证产生次优解,极难求得最优解等问题,提出一种基于梯度策略的多目标GANs帕累托最优解算法。该算法采用硬参数共享方式,将多目标优化分解为多个两目标优化,确定多目标权重参数后,沿着梯度方向进行线性搜索,最终确定帕累托最优解。理论上,在弱条件约束下,证明了所提算法能够确切地产生帕累托最优解。实验上,将所提算法应用到图像处理的常见领域,对比所提算法与原算法的性能。结果表明,当目标数量大于2时,所提算法能够产生明显的性能优势。