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SG++: SG++: General Sparse Grid Toolbox

SG : General Sparse Grid Toolbox. SG is a universal toolbox for spatially adaptive sparse grid methods and the combination technique. It provides various low-level and high-level sparse grid functionality allowing one to start using sparse grids with minimal initial implementation effort. The functionality. Ranges from interpolation and quadrature via the solution of differential equations to regression, classification, and more. The software architecture of the SG toolbox ensures modularity. If you are ...

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SG++: SG++: General Sparse Grid Toolbox | sparsegrids.org Reviews

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SG : General Sparse Grid Toolbox. SG is a universal toolbox for spatially adaptive sparse grid methods and the combination technique. It provides various low-level and high-level sparse grid functionality allowing one to start using sparse grids with minimal initial implementation effort. The functionality. Ranges from interpolation and quadrature via the solution of differential equations to regression, classification, and more. The software architecture of the SG toolbox ensures modularity. If you are ...

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SG++: SG++: General Sparse Grid Toolbox | sparsegrids.org Reviews

https://sparsegrids.org

SG : General Sparse Grid Toolbox. SG is a universal toolbox for spatially adaptive sparse grid methods and the combination technique. It provides various low-level and high-level sparse grid functionality allowing one to start using sparse grids with minimal initial implementation effort. The functionality. Ranges from interpolation and quadrature via the solution of differential equations to regression, classification, and more. The software architecture of the SG toolbox ensures modularity. If you are ...

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sgpp.sparsegrids.org

SG++: SG++: General Sparse Grid Toolbox

SG : General Sparse Grid Toolbox. SG is a universal toolbox for spatially adaptive sparse grid methods and the combination technique. It provides various low-level and high-level sparse grid functionality allowing one to start using sparse grids with minimal initial implementation effort. The functionality. Ranges from interpolation and quadrature via the solution of differential equations to regression, classification, and more. The software architecture of the SG toolbox ensures modularity. If you are ...

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sparsegrids.org

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SG++: Developer Manual

http://www.sparsegrids.org/development.html

On this page, we describe best coding practices for SG . All code has to be documented in such a way that functions, classes, and all other entities appearing in this documentation are easily understandable for. We have both Boost unit tests (in C ) and Python unit tests. The tests can be found in the test subdirectory of each module. New tests should be written preferably with Boost. However, there is some extra functionality in Python which can be tested only in Python, of course. The complete code has...

2

SG++: Modules

http://www.sparsegrids.org/modules.html

To insure extendability and maintainability, SG is divided into a number of modules that implement different functionality. Different modules can be compiled independently from each other. The modules can depend on each other. Especially all depend on the base module. Libraries are generated for all modules. The following modules are available:. Fundamental functionality required by all other modules. Data mining and machine learning. SG module for optimization of smooth sparse grid interpolants.

3

SG++: Namespace List

http://www.sparsegrids.org/namespaces.html

Here is a list of all namespaces with brief descriptions:. Bilinear form admissible set. SG module for optimization of smooth sparse grid interpolants. Namespace with functions to write data (vectors, matrices, grids, .) to a file. Namespace with linear algebra functions. Namespace containing methods for gradient-based and gradient-free optimization. Namespace containing classes for solving linear systems. Namespace containing explicit test problems. Generated on Thu Mar 30 2017 11:07:44 for SG by.

4

SG++: Class List

http://www.sparsegrids.org/annotated.html

Here are the classes, structs, unions and interfaces with brief descriptions:. Class for handling events for storing and restoring of checkpoints. The class processes the progress information from Learner and LinearSolver and stores it into the file. This class processes the information about the current state of the learning process and presents it in form of a graph. The class processes the progress information from Learner and LinearSolver and shows it on the terminal screen. And allows to accomplish ...

5

SG++: Linux (GCC/Clang/ICC)

http://www.sparsegrids.org/linux.html

This page contains instructions for compiling and using SG with GCC, Clang or ICC under Linux. For brevity, we assume you want to compile with GCC; the compiler can be changed easily, however. The following software is required in order to build SG :. GCC (≥ 4.8). Or Clang (≥ 3.8). SCons (≥ 2.3). Is required for building (build system). SCons is written in Python 2.7, which is therefore needed by SG as well. The following software is recommended for core functionality:. SWIG (≥ 3.0). On a recent Ubuntu s...

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sparsegrids.org

SG++: SG++: General Sparse Grid Toolbox

SG : General Sparse Grid Toolbox. SG is a universal toolbox for spatially adaptive sparse grid methods and the combination technique. It provides various low-level and high-level sparse grid functionality allowing one to start using sparse grids with minimal initial implementation effort. The functionality. Ranges from interpolation and quadrature via the solution of differential equations to regression, classification, and more. The software architecture of the SG toolbox ensures modularity. If you are ...

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