368 research outputs found
Submodular Welfare Maximization
An overview of different variants of the submodular welfare maximization
problem in combinatorial auctions. In particular, I studied the existing
algorithmic and game theoretic results for submodular welfare maximization
problem and its applications in other areas such as social networks
Submodular Load Clustering with Robust Principal Component Analysis
Traditional load analysis is facing challenges with the new electricity usage
patterns due to demand response as well as increasing deployment of distributed
generations, including photovoltaics (PV), electric vehicles (EV), and energy
storage systems (ESS). At the transmission system, despite of irregular load
behaviors at different areas, highly aggregated load shapes still share similar
characteristics. Load clustering is to discover such intrinsic patterns and
provide useful information to other load applications, such as load forecasting
and load modeling. This paper proposes an efficient submodular load clustering
method for transmission-level load areas. Robust principal component analysis
(R-PCA) firstly decomposes the annual load profiles into low-rank components
and sparse components to extract key features. A novel submodular cluster
center selection technique is then applied to determine the optimal cluster
centers through constructed similarity graph. Following the selection results,
load areas are efficiently assigned to different clusters for further load
analysis and applications. Numerical results obtained from PJM load demonstrate
the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G
Bounding the Greedy Strategy in Finite-Horizon String Optimization
We consider an optimization problem where the decision variable is a string
of bounded length. For some time there has been an interest in bounding the
performance of the greedy strategy for this problem. Here, we provide weakened
sufficient conditions for the greedy strategy to be bounded by a factor of
, where is the optimization horizon length. Specifically, we
introduce the notions of -submodularity and -GO-concavity, which together
are sufficient for this bound to hold. By introducing a notion of
\emph{curvature} , we prove an even tighter bound with the factor
. Finally, we illustrate the strength of our results by
considering two example applications. We show that our results provide weaker
conditions on parameter values in these applications than in previous results.Comment: This paper has been accepted by 2015 IEEE CD
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