The NTIS website and supporting ordering systems are undergoing a major upgrade from 8PM on September 25th through approximately October 6. During that time, much of the functionality, including subscription and product ordering, shipping, etc., will not be available. You may call NTIS at 1-800-553-6847 or (703) 605-6000 to place an order but you should expect delayed shipment. Please do NOT include credit card numbers in any email you might send NTIS.
Documents in the NTIS Technical Reports collection are the results of federally funded research. They are directly submitted to or collected by NTIS from Federal agencies for permanent accessibility to industry, academia and the public.  Before purchasing from NTIS, you may want to check for free access from (1) the issuing organization's website; (2) the U.S. Government Printing Office's Federal Digital System website; (3) the federal government Internet portal; or (4) a web search conducted using a commercial search engine such as
Accession Number ADA585471
Title Trace-Penalty Minimization for Large-scale Eigenspace Computation.
Publication Date Mar 2013
Media Count 31p
Personal Author C. Yang X. Liu Y. Zhang Z. Wen
Abstract The Rayleigh-Ritz (RR) procedure, including orthogonalization, constitutes a major bottleneck in computing relatively high-dimensional eigenspaces of large sparse matrices. Although operations involved in RR steps can be parallelized to a certain level, their parallel scalability, which is limited by some inherent sequential steps, is lower than dense matrix-matrix multiplications. The primary motivation of this paper is to develop a methodology that reduces the use of the RR procedure in exchange for matrix- matrix multiplications. We propose an unconstrained penalty model and establish its equivalence to the eigenvalue problem. This model enables us to deploy gradient-type algorithms that makes heavy use of dense matrix-matrix multiplications. Although the proposed algorithm does not necessarily reduce the total number of arithmetic operations, it leverages highly optimized operations on modern high performance computers to achieve parallel scalability. Numerical results based on a preliminary implementation, parallelized using OpenMP, show that our approach is promising.
Keywords Algorithms
Eigenvalue computation
Exact quadratic penalty approach
Gradient methods
High performance computing

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
Corporate Author Rice Univ., Houston, TX. Dept. of Computational and Applied Mathematics.
Document Type Technical report
Title Note N/A
NTIS Issue Number 1403
Contract Number N00014-08-1-1101

Science and Technology Highlights

See a sampling of the latest scientific, technical and engineering information from NTIS in the NTIS Technical Reports Newsletter

Acrobat Reader Mobile    Acrobat Reader