Local implementation of nonlocal operations of block forms

We investigate the local implementation of nonlocal operations with the block matrix form, and propose a protocol for any diagonal or offdiagonal block operation. This method can be directly generalized to the two-party multiqubit case and the multiparty case. Especially, in the multiparty cases, any diagonal block operation can be locally implemented using the same resources as the multiparty control-U operation discussed in Ref. [Eisert et al., Phys. Rev. A 62, 052317(2000)]. Although in the bipartite case, this kind of operations can be transformed to control-U operation using local operations, these transformations are impossible in the multiparty cases. We also compare the local implementation of nonlocal block operations with the remote implementation of local operations, and point out a relation between them.Comment: 7 pages, 3 figure

A 8-neighbor model lattice Boltzmann method applied to mathematical-physical equations

© 2016. This version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A lattice Boltzmann method (LBM) 9-bit model is presented to solve mathematical-physical equations, such as, Laplace equation, Poisson equation, Wave equation and Burgers equation. The 9-bit model has been verified by several test cases. Numerical simulations, including 1D and 2D cases, of each problem are shown respectively. Comparisons are made between numerical predictions and analytic solutions or available numerical results from previous researchers. It turned out that the 9-bit model is computationally effective and accurate for all different mathematical-physical equations studied. The main benefits of the new model proposed is that it is faster than the previous existing models and has a better accuracy.Peer ReviewedPostprint (author's final draft

Numerical study of the 2D lid-driven triangular cavities based on the Lattice Boltzmann method

Numerical study of two dimensional lid driven triangular cavity flow is performed via using lattice Boltzmann method on low Reynolds numbers. The equilateral triangular cavity is the first geometry to be studied, the simulation is performed at Reynolds number 500 and the numerical prediction is compared with previous work done by other scholars. Several isosceles triangular cavities are studied at different initial conditions, Reynolds numbers ranging from 100 to 3000, regardless of the geometry studied, the top lid is always moving from left to right and the driven velocity remains constant. Results are also compared with previous work performed by other scholars, the agreement is very good. According to the authors’ knowledge, this is the first time that MRT-LBM model is used to simulate the flow inside the triangular cavities. One of the advantages of this method is that it is capable of producing at low and high Reynolds numbers.Peer ReviewedPostprint (published version

Chance Constrained Mixed Integer Program: Bilinear and Linear Formulations, and Benders Decomposition

In this paper, we study chance constrained mixed integer program with consideration of recourse decisions and their incurred cost, developed on a finite discrete scenario set. Through studying a non-traditional bilinear mixed integer formulation, we derive its linear counterparts and show that they could be stronger than existing linear formulations. We also develop a variant of Jensen's inequality that extends the one for stochastic program. To solve this challenging problem, we present a variant of Benders decomposition method in bilinear form, which actually provides an easy-to-use algorithm framework for further improvements, along with a few enhancement strategies based on structural properties or Jensen's inequality. Computational study shows that the presented Benders decomposition method, jointly with appropriate enhancement techniques, outperforms a commercial solver by an order of magnitude on solving chance constrained program or detecting its infeasibility

Collaboration based Multi-Label Learning

It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1

Automated Negotiation for Complex Multi-Agent Resource Allocation

The problem of constructing and analyzing systems of intelligent, autonomous agents is becoming more and more important. These agents may include people, physical robots, virtual humans, software programs acting on behalf of human beings, or sensors. In a large class of multi-agent scenarios, agents may have different capabilities, preferences, objectives, and constraints. Therefore, efficient allocation of resources among multiple agents is often difficult to achieve. Automated negotiation (bargaining) is the most widely used approach for multi-agent resource allocation and it has received increasing attention in the recent years. However, information uncertainty, existence of multiple contracting partners and competitors, agents\u27 incentive to maximize individual utilities, and market dynamics make it difficult to calculate agents\u27 rational equilibrium negotiation strategies and develop successful negotiation agents behaving well in practice. To this end, this thesis is concerned with analyzing agents\u27 rational behavior and developing negotiation strategies for a range of complex negotiation contexts. First, we consider the problem of finding agents\u27 rational strategies in bargaining with incomplete information. We focus on the principal alternating-offers finite horizon bargaining protocol with one-sided uncertainty regarding agents\u27 reserve prices. We provide an algorithm based on the combination of game theoretic analysis and search techniques which finds agents\u27 equilibrium in pure strategies when they exist. Our approach is sound, complete and, in principle, can be applied to other uncertainty settings. Simulation results show that there is at least one pure strategy sequential equilibrium in 99.7% of various scenarios. In addition, agents with equilibrium strategies achieved higher utilities than agents with heuristic strategies. Next, we extend the alternating-offers protocol to handle concurrent negotiations in which each agent has multiple trading opportunities and faces market competition. We provide an algorithm based on backward induction to compute the subgame perfect equilibrium of concurrent negotiation. We observe that agents\u27 bargaining power are affected by the proposing ordering and market competition and for a large subset of the space of the parameters, agents\u27 equilibrium strategies depend on the values of a small number of parameters. We also extend our algorithm to find a pure strategy sequential equilibrium in concurrent negotiations where there is one-sided uncertainty regarding the reserve price of one agent. Third, we present the design and implementation of agents that concurrently negotiate with other entities for acquiring multiple resources. Negotiation agents are designed to adjust 1) the number of tentative agreements and 2) the amount of concession they are willing to make in response to changing market conditions and negotiation situations. In our approach, agents utilize a time-dependent negotiation strategy in which the reserve price of each resource is dynamically determined by 1) the likelihood that negotiation will not be successfully completed, 2) the expected agreement price of the resource, and 3) the expected number of final agreements. The negotiation deadline of each resource is determined by its relative scarcity. Since agents are permitted to decommit from agreements, a buyer may make more than one tentative agreement for each resource and the maximum number of tentative agreements is constrained by the market situation. Experimental results show that our negotiation strategy achieved significantly higher utilities than simpler strategies. Finally, we consider the problem of allocating networked resources in dynamic environment, such as cloud computing platforms, where providers strategically price resources to maximize their utility. While numerous auction-based approaches have been proposed in the literature, our work explores an alternative approach where providers and consumers negotiate resource leasing contracts. We propose a distributed negotiation mechanism where agents negotiate over both a contract price and a decommitment penalty, which allows agents to decommit from contracts at a cost. We compare our approach experimentally, using representative scenarios and workloads, to both combinatorial auctions and the fixed-price model, and show that the negotiation model achieves a higher social welfare

Optimal Posted Prices for Online Cloud Resource Allocation

We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool