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Project Information

AF: SMALL: INCREMENTAL AND ASYNCHRONOUS PROJECTIVE SPLITTING METHODS FOR MATHEMATICAL PROGRAMMING

Agency:
NSF

National Science Foundation

Project Number:
1617617
Contact PI / Project Leader:
ECKSTEIN, JONATHAN
Awardee Organization:
RUTGERS THE STATE UNIV OF NJ NEWARK

Description

Abstract Text:
A key to solving large computational and mathematical problems, such as analyzing large datasets or planning for the operation of an electrical power grid or any other complicated systems with an uncertain future demands and supplies, is decomposing into smaller solvable subproblems or subsystems, then coordinating and integrating their results, and decomposing again into adjusted subproblems. Properly designed decomposition methods repeat a decomposition - coordination cycle that converge to the solution of the entire original, non-decomposed problem. The PI is working with Sandia National Laboratories and has particular interest in problems that arise in operating electrical power grids with high penetration of renewable generation sources, like solar and wind, where weather has unplanned affects the supply. This project studies a new way to perform decomposition, called "incremental projective operator splitting" (IPOS) or "block-iterative splitting." It is related to a popular decomposition method called the alternating direction method of multipliers (ADMM) but is far more flexible. While essentially all prior decomposition methods follow a rigid cycle of decomposition and coordination steps, with every decomposition step encompassing all the subsystems, the new method has much greater flexibility: only a subset of subsystems need to be considered between coordination steps, and decomposition and coordination calculations can overlap asynchronously. This flexibility should allow more efficient use of parallel computers by eliminating rigid synchronization points. This property is important because most future growth in computer performance is anticipated to result from larger numbers of parallel processing units, and only parallel computers will be able to manipulate the large datasets and decision problems we hope to analyze. Because the new IPOS methods are so flexible, there are numerous ways in which they could be used on the same class of problems. The main goal of this project is to develop and experimentally evaluate strategies for applying IPOS on parallel computers. It will focus on two common problem classes, large-scale data analysis and planning under uncertainty, using real-world input data to the maximum practical extent. Other research topics include sharpening the mathematical theory of IPOS, and extending this theory to cover a broader range of problems, and development and release of software based on this new theory.
Project Terms:
Affect; base; Computer software; Computers; Data; Data Analyses; Data Set; design; Development; flexibility; Future; Generations; Goals; Growth; interest; Laboratories; mathematical methods; mathematical theory; Methods; operation; parallel computer; parallel processing; Penetration; Performance; programs; Property; Research; solar wind; Source; System; theories; Uncertainty; Weather; Work

Details

Contact PI / Project Leader Information:
Name:  ECKSTEIN, JONATHAN
Other PI Information:
Not Applicable
Awardee Organization:
Name:  RUTGERS THE STATE UNIV OF NJ NEWARK
City:  NEWARK    
Country:  UNITED STATES
Congressional District:
State Code:  NJ
District:  10
Other Information:
Fiscal Year: 2016
Award Notice Date: 01-Aug-2016
DUNS Number: 130029205
Project Start Date: 01-Sep-2016
Budget Start Date:
CFDA Code: 47.070
Project End Date: 31-Aug-2019
Budget End Date:
Agency: ?

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National Science Foundation
Project Funding Information for 2016:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

FY Total Cost
2016 NSF

National Science Foundation

$457,072

Results

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