(redirected from Conjugate gradient method)
Also found in: Wikipedia.
CGMComputer Graphics Metafile (file format and file extension)
CGMContinuous Glucose Monitoring (for diabetes)
CGMConsumer-Generated Media
CGMCitigroup Global Markets (est. 2003)
CGMCalouste Gulbenkian Museum (Portugal)
CGMComputer Graphics Metafile
CGMComputer Generated Model
CGMComputer Generated Maps
CGMChief General Manager
CGMCountry General Manager (various companies)
CGMChurch Growth Movement
CGMConspicuous Gallantry Medal
CGMConseil Général des Mines (French: General Council of Mines)
CGMCentre de Génétique Moléculaire (French: Center for Molecular Genetics)
CGMComputer Games Magazine
CGMConjugate Gradient Method
CGMCorrected Geomagnetic Coordinates
CGMChief Gunner's Mate (rank; US Navy)
CGMChina Grill Management (restaurant group)
CGMCombustible Gas Monitor (gas leak detection)
CGMCommon Geometry Module
CGMCompagnie General Maritime (French: General Maritime Company; global carrier; Marseille, France)
CGMCountry Gospel Music
CGMCenter for Green Manufacturing (University of Alabama)
CGMConstrained Global Minima
CGMContinuous Ground Monitor (extension cords)
CGMClinique Genevoise de Montana (French; public medical rehabilitation clinic; Switzerland)
CGMCanadians for a Genocide Museum (est. 1998)
CGMCard Games Masters (Russian poker website)
CGMContracted Gross Margin
CGMCoastguard Minute (UK)
CGMCommon GPS (Global Positioning System) Module (US DoD)
References in periodicals archive ?
8 compare the FFT based algorithm and the preconditioned conjugate gradient method.
Recently, Richardson method [7], conjugate gradient method [8], and GS method [9] are all applied to solve (10) in an iterative way.
The goal was to improve the convergence of parallelized conjugate gradient method after preconditioning by pseudo-cliques balancing.
In this section, we consider the preconditioned global conjugate gradient method (PGCG) for solving the least squares reduced problem (4.
A modified scaled memoryless BFGS preconditioned conjugate gradient method for unconstrained optimization.
One is the inner iteration that completes the source reconstruction by solving (7) via the conjugate gradient method.
The good point about Conjugate Gradient method is that it automatically generates direction vectors at the previous step.
The conjugate gradient method and trust regions in large scale optimization.
Interest in the conjugate gradient method surged again in the 1970s when researchers discovered new variants and successful techniques for preconditioning the problem (i.
They cover norms and perturbation analysis, least squares problems, generalized inverses, the conjugate gradient method, optimal and super-optimal preconditioners, optimal preconditioners for functions of matrices, and B|ttcher-Wenzel conjecture and related problems.
It is preferred to use the conjugate gradient method to help to speed up convergence: