On the Performance of Approximate Equilibria in Congestion Games

We study the performance of approximate Nash equilibria for congestion games with polynomial latency functions. We consider how much the price of anarchy worsens and how much the price of stability improves as a function of the approximation factor ε. We give tight bounds for the price of anarchy of atomic and non-atomic congestion games and for the price of stability of non-atomic congestion games. For the price of stability of atomic congestion games we give non-tight bounds for linear latencies. Our results not only encompass and generalize the existing results of exact equilibria to ε-Nash equilibria, but they also provide a unified approach which reveals the common threads of the atomic and non-atomic price of anarchy results. By expanding the spectrum, we also cast the existing results in a new light.     

Characterizing the Existence of Potential Functions in Weighted Congestion Games

Since the pioneering paper of Rosenthal a lot of work has been done in order to determine classes of games that admit a potential. First, we study the existence of potential functions for weighted congestion games. Let \(\mathcal{C}\) be an arbitrary set of locally bounded functions and let \(\mathcal{G}(\mathcal{C})\) be the set of weighted congestion games with cost functions in \(\mathcal{C}\). We show that every weighted congestion game \(G\in\mathcal{G}(\mathcal{C})\) admits an exact potential if and only if \(\mathcal{C}\) contains only affine functions. We also give a similar characterization for w-potentials with the difference that here \(\mathcal{C}\) consists either of affine functions or of certain exponential functions. We finally extend our characterizations to weighted congestion games with facility-dependent demands and elastic demands, respectively.     

Atomic Congestion Games: Fast, Myopic and Concurrent

We study here the effect of concurrent greedy moves of players in atomic congestion games where n selfish agents (players) wish to select a resource each (out of m resources) so that her selfish delay there is not much. The problem of “maintaining” global progress while allowing concurrent play is exactly what is examined and answered here. We examine two orthogonal settings: (i) A game where the players decide their moves without global information, each acting “freely” by sampling resources randomly and locally deciding to migrate (if the new resource is better) via a random experiment. Here, the resources can have quite arbitrary latency that is load dependent. (ii) An “organised” setting where the players are pre-partitioned into selfish groups (coalitions) and where each coalition does an improving coalitional move. Our work considers concurrent selfish play for arbitrary latencies for the first time. Also, this is the first time where fast coalitional convergence to an approximate equilibrium is shown.     

On the Complexity of Iterated Weak Dominance in Constant-Sum Games

In game theory, an action is said to be weakly dominated if there exists another action of the same player that, with respect to what the other players do, is never worse and sometimes strictly better. We investigate the computational complexity of the process of iteratively eliminating weakly dominated actions (IWD) in two-player constant-sum games, i.e., games in which the interests of both players are diametrically opposed. It turns out that deciding whether an action is eliminable via IWD is feasible in polynomial time whereas deciding whether a given subgame is reachable via IWD is NP-complete. The latter result is quite surprising, as we are not aware of other natural computational problems that are intractable in constant-sum normal-form games. Furthermore, we slightly improve on a result of V. Conitzer and T. Sandholm by showing that typical problems associated with IWD in win-lose games with at most one winner are NP-complete.     

Performance Evaluation of Optimal Linear Systems†

The parameter variation problem of optimal linear control systems with quadratic performance indices is considered. The change in performance due to persistent changes in parameters is easily evaluated by considering the performance index as the Lyapunov function. Assuming the control law to be unchanged, parameter variations that cause system performance deterioration or improvement, are studied on the parameter plane. The bounds on performance are also given. The last example deals with a piecewise linear system.     

Suboptimal Strategies of Linear Quadratic Closed-loop Differential Games: An BMI Approach

The suboptimal control program via memoryless state feedback strategies for LQ differential games with multiple players is studied in this paper. Sufficient conditions for the existence of the suboptimal strategies for LQ differential games are presented. It is shown that the suboptimal strategies of LQ differential games are associated with a coupled algebraic Riccati inequality. Furthermore, the problem of designing suboptimal strategies is considered. A non-convex optimization problem with BMI constrains is formulated to design the suboptimal strategies which minimizes the performance indices of the closed-loop LQ differential games and can be solved by using LMI Toolbox of MATLAB, An example is given to illustrate the proposed results.     

Sensitivity of the Performance of Optimal Linear Control Systems to Parameter Variations†

The sensitivity of the index of performance to parameter variations in optimal control systems is examined in this paper. It is shown, in the case of linear optimal systems with quadratic performance criteria, that the value of the performance index, after the system parameters have deviated from a nominal set of values, is still given by a symmetric positive definite quadratic form of the initial state. The matrix of this quadratic form is governed by a special case of the matrix Riccati equation. It is shown also, that similar results hold for the performance index sensitivity function.

Because the sensitivity problem closely parallels the original optimization problem, the computational techniques used in the design of the optimal system may be reapplied in the sensitivity analysis.     

Linear Time Algorithms for Generalizations of the Longest Common Substring Problem

In its simplest form, the longest common substring problem is to find a longest substring common to two or multiple strings. Using (generalized) suffix trees, this problem can be solved in linear time and space. A first generalization is the k -common substring problem: Given m strings of total length n, for all k with 2≤k≤m simultaneously find a longest substring common to at least k of the strings. It is known that the k-common substring problem can also be solved in O(n) time (Hui in Proc. 3rd Annual Symposium on Combinatorial Pattern Matching, volume 644 of Lecture Notes in Computer Science, pp. 230–243, Springer, Berlin, 1992). A further generalization is the k -common repeated substring problem: Given m strings T ⁽¹⁾,T ⁽²⁾,…,T ^(m) of total length n and m positive integers x ₁,…,x _m , for all k with 1≤k≤m simultaneously find a longest string ω for which there are at least k strings \(T^{(i_{1})},T^{(i_{2})},\ldots,T^{(i_{k})}\) (1≤i ₁<i ₂<???<i _k ≤m) such that ω occurs at least \(x_{i_{j}}\) times in \(T^{(i_{j})}\) for each j with 1≤j≤k. (For x ₁=???=x _m =1, we have the k-common substring problem.) In this paper, we present the first O(n) time algorithm for the k-common repeated substring problem. Our solution is based on a new linear time algorithm for the k-common substring problem.     

Robust H ∞ Control of Linear Uncertain Systems: Its Limiting Performance

The concept of a H norm is introduced for linear control systems with uncertain bounded parameters. A procedure for determining the lower estimate of the minimal robust H norm in the linear state feedback class is designed. An example of a system in which the estimate coincides with the minimal robust norm is given.     

Performance evaluation of cloud service considering fault recovery

Cloud computing is a recent trend in IT, which has attracted lots of attention. In cloud computing, service reliability and service performance are two important issues. To improve cloud service reliability, fault tolerance techniques such as fault recovery may be used, which in turn has impact on cloud service performance. Such impact deserves detailed research. Although there exist some researches on cloud/grid service reliability and performance, very few of them addressed the issues of fault recovery and its impact on service performance. In this paper, we conduct detailed research on performance evaluation of cloud service considering fault recovery. We consider recovery on both processing nodes and communication links. The commonly adopted assumption of Poisson arrivals of users’ service requests is relaxed, and the interarrival times of service requests can take arbitrary probability distribution. The precedence constraints of subtasks are also considered. The probability distribution of service response time is derived, and a numerical example is presented. The proposed cloud performance evaluation models and methods could yield results which are realistic, and thus are of practical value for related decision-makings in cloud computing.     

Congestion control algorithm in large-delay uncertain networks

Based on Smith-fuzzy controller, a new active queue management （AQM） algorithm adaptable to the large-delay uncertain networks is presented. It can compensate the negative impact on the queue stability caused by the large delay, and it also maintains strong robustness under the condition of dynamic network fluid. Its stability is proven through Lyapunov method. Simulation results demonstrated that this method enables the queue length to converge at a preset value quickly and keeps the queue oscillation small, the simulation results also show that the scheme is very robust to disturbance under various network conditions and large delay and, in particular, the algorithm proposed outperforms the conventional PI control and fuzzy control when the network parameters and network delay change.     

Discontinuous Hamiltonian Finite Element Method for Linear Hyperbolic Systems

We develop a Hamiltonian discontinuous finite element discretization of a generalized Hamiltonian system for linear hyperbolic systems, which include the rotating shallow water equations, the acoustic and Maxwell equations. These equations have a Hamiltonian structure with a bilinear Poisson bracket, and as a consequence the phase-space structure, “mass” and energy are preserved. We discretize the bilinear Poisson bracket in each element with discontinuous elements and introduce numerical fluxes via integration by parts while preserving the skew-symmetry of the bracket. This automatically results in a mass and energy conservative discretization. When combined with a symplectic time integration method, energy is approximately conserved and shows no drift. For comparison, the discontinuous Galerkin method for this problem is also used. A variety numerical examples is shown to illustrate the accuracy and capability of the new method.     

Evolution of Probability Distribution in Time for Solutions of Hyperbolic Equations

We investigate the evolution of the probability distribution function in time for some wave and Maxwell equations in random media for which the parameters, e.g. permeability, permittivity, fluctuate randomly in space; more precisely, two different media interface randomly in space. We numerically compute the probability distribution and density for output solutions. The underlying numerical and statistical techniques are the so-called polynomial chaos Galerkin projection, which has been extensively used for simulating partial differential equations with uncertainties, and the Monte Carlo simulations.     

Context,Computation, and Optimal ROC Performance in Hierarchical Models

It is widely recognized that human vision relies on contextual information, typically arising from each of many levels of analysis. Local gradient information, otherwise ambiguous, is seen as part of a smooth contour or sharp angle in the context of an object’s boundary or corner. A stroke or degraded letter, unreadable by itself, contributes to the perception of a familiar word in the context of the surrounding strokes and letters. The iconic Dalmatian dog stays invisible until a multitude of clues about body parts and posture, and figure and ground, are coherently integrated. Context is always based on knowledge about the composition of parts that make up a whole, as in the arrangement of strokes that make up a letter, the arrangement of body parts that make up an animal, or the poses and postures of individuals that make up a mob. From this point of view, the hierarchy of contextual information available to an observer derives from the compositional nature of the world being observed. We will formulate this combinatorial viewpoint in terms of probability distributions and examine the computational implications. Whereas optimal recognition performance in this formulation is NP-complete, we will give mathematical and experimental evidence that a properly orchestrated computational algorithm can achieve nearly optimal recognition within a feasible number of operations. We will interpret the notions of bottom-up and top-down processing as steps in the staging of one such orchestration.     

Minimum Augmentation of Edge-Connectivity between Vertices and Sets of Vertices in Undirected Graphs

Given an undirected multigraph G=(V,E), a family $\mathcal{W}Given an undirected multigraph G=(V,E), a family W\mathcal{W} of areas W⊆V, and a target connectivity k≥1, we consider the problem of augmenting G by the smallest number of new edges so that the resulting graph has at least k edge-disjoint paths between v and W for every pair of a vertex v∈V and an area W ? WW\in \mathcal{W} . So far this problem was shown to be NP-complete in the case of k=1 and polynomially solvable in the case of k=2. In this paper, we show that the problem for k≥3 can be solved in O(m+n(k ³+n ²)(p+kn+nlog n)log k+pkn ³log (n/k)) time, where n=|V|, m=|{{u,v}|(u,v)∈E}|, and p=|W|p=|\mathcal{W}| .     

System reduction in multibody dynamics of wind turbines

A system reduction scheme is devised related to a multibody formulation from which the dynamic response of a wind turbine is determined. In this formulation, each substructure is described in its own frame of reference, which is moving freely in the vicinity of the moving substructure. The Ritz bases spanning the reduced system comprises of rigid body modes and some dynamic low-frequency elastic eigenmodes compatible to the kinematic constraints of the related substructure. The high-frequency elastic modes are presumed to cause merely quasi-static displacements, and thus are included in the expansion via a quasi-static correction. The results show that by using the derived reduction scheme it is only necessary with 2 dynamical modes for the blade substructure when the remaining modes are treated as quasi-static. Moreover, it is shown that it has little to none effect if the gyroscopic stiffness matrix during a stopped situation or under nominal operational conditions is used to derive the functional basis of the modal expansion.     

A Bilinear Approach to the Parameter Estimation of a General Heteroscedastic Linear System,with Application to Conic Fitting

In this paper, we employ low-rank matrix approximation to solve a general parameter estimation problem: where a non-linear system is linearized by treating the carrier terms as separate variables, thereby introducing heteroscedastic noise. We extend the bilinear approach to handle cases with heteroscedastic noise, in the framework of low-rank approximation. The ellipse fitting problem is investigated as a specific example of the general theory. Despite the impression given in the literature, the ellipse fitting problem is still unsolved when the data comes from a small section of the ellipse. Although there are already some good approaches to the problem of ellipse fitting, such as FNS and HEIV, convergence in these iterative approaches is not ensured, as pointed out in the literature. Another limitation of these approaches is that they cannot model the correlations among different rows of the “general measurement matrix”. Our method, of employing the bilinear approach to solve the general heteroscedastic parameter estimation problem, overcomes these limitations: it is convergent, at least to a local optimum, and can cope with a general heteroscedastic problem. Experiments show that the proposed bilinear approach performs better than other competing approaches: although it is still far short of a solution when the data comes from a very small arc of the ellipse.

Performance analysis of active schedules in identical parallel machine

Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs’ dynamic arrivals, the tight performance bounds of active schedules under the measurement of four popular objectives are respectively given in this paper. Similar analysis method and conclusions can be generalized to static identical parallel machine and single machine scheduling problem.     

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the Internet.     

Evaluation of gang scheduling performance and cost in a cloud computing system

Cloud Computing refers to the notion of outsourcing on-site available services, computational facilities, or data storage to an off-site, location-transparent centralized facility or “Cloud.” Gang Scheduling is an efficient job scheduling algorithm for time sharing, already applied in parallel and distributed systems. This paper studies the performance of a distributed Cloud Computing model, based on the Amazon Elastic Compute Cloud (EC2) architecture that implements a Gang Scheduling scheme. Our model utilizes the concept of Virtual Machines (or VMs) which act as the computational units of the system. Initially, the system includes no VMs, but depending on the computational needs of the jobs being serviced new VMs can be leased and later released dynamically. A simulation of the aforementioned model is used to study, analyze, and evaluate both the performance and the overall cost of two major gang scheduling algorithms. Results reveal that Gang Scheduling can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.