1,094 research outputs found
The Isomorphism Problem for Higman-Thompson groups
We prove that the HigmanâThompson groups View the MathML sourceGn,r+ and View the MathML sourceGm,s+ are isomorphic if and only if m=nm=n and gcd(nâ1,r)=gcd(nâ1,s)gcd(nâ1,r)=gcd(nâ1,s)
Accelerating Incremental Gradient Optimization with Curvature Information
This paper studies an acceleration technique for incremental aggregated
gradient ({\sf IAG}) method through the use of \emph{curvature} information for
solving strongly convex finite sum optimization problems. These optimization
problems of interest arise in large-scale learning applications. Our technique
utilizes a curvature-aided gradient tracking step to produce accurate gradient
estimates incrementally using Hessian information. We propose and analyze two
methods utilizing the new technique, the curvature-aided IAG ({\sf CIAG})
method and the accelerated CIAG ({\sf A-CIAG}) method, which are analogous to
gradient method and Nesterov's accelerated gradient method, respectively.
Setting to be the condition number of the objective function, we prove
the linear convergence rates of for
the {\sf CIAG} method, and for the {\sf
A-CIAG} method, where are constants inversely proportional to
the distance between the initial point and the optimal solution. When the
initial iterate is close to the optimal solution, the linear convergence
rates match with the gradient and accelerated gradient method, albeit {\sf
CIAG} and {\sf A-CIAG} operate in an incremental setting with strictly lower
computation complexity. Numerical experiments confirm our findings. The source
codes used for this paper can be found on
\url{http://github.com/hoitowai/ciag/}.Comment: 22 pages, 3 figures, 3 tables. Accepted by Computational Optimization
and Applications, to appea
Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks
We study the system-level effects of the introduction of large populations of
Electric Vehicles on the power and transportation networks. We assume that each
EV owner solves a decision problem to pick a cost-minimizing charge and travel
plan. This individual decision takes into account traffic congestion in the
transportation network, affecting travel times, as well as as congestion in the
power grid, resulting in spatial variations in electricity prices for battery
charging. We show that this decision problem is equivalent to finding the
shortest path on an "extended" transportation graph, with virtual arcs that
represent charging options. Using this extended graph, we study the collective
effects of a large number of EV owners individually solving this path planning
problem. We propose a scheme in which independent power and transportation
system operators can collaborate to manage each network towards a socially
optimum operating point while keeping the operational data of each system
private. We further study the optimal reserve capacity requirements for pricing
in the absence of such collaboration. We showcase numerically that a lack of
attention to interdependencies between the two infrastructures can have adverse
operational effects.Comment: Submitted to IEEE Transactions on Control of Network Systems on June
1st 201
Optimal Electricity Pricing for Societal Infrastructure Systems
We develop a general framework for pricing electricity in order to optimally manage the electricity load of societal infrastructures that interact with power systems through their price-responsive electricity load. In such infrastructure systems, electricity is not the sole resource needed to serve users\u27 needs. Examples include cloud computing infrastructure or electric transportation networks. In these cases, other shared networked resources such as charging stations or communication links and data centers are also required to serve users. Hence, pricing of electricity becomes intertwined to managing other congestible resources not priced by the power system operator, leading to a complex economic dispatch problem. For brevity of notation, our analysis is performed under a static setting. We discuss how the power system operator should model the effects of the mobility of loads and congestion in the infrastructure in the economic dispatch. We numerically study the performance of our algorithms using the example of a simple electric transportation network
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