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[3.4.0] - 2021-06-28

Added

• New B-spline bases
  • Extended not-a-knot B-splines
  • Boundaryless not-a-knot B-splines
• New convenience classes for surrogates and response surfaces
  • ResponseSurface
  • SplineResponseSurface
  • SplineResponseSurfaceVector
• Vector refinement functors
  • VectorSurplusRefinementFunctor
  • VectorDistributionRefinementFunctor
• OperationWeightedQuadrature for the calculation of means
• OperationWeightedSecondMoment for the calculation of variances
• New methods for the datamining pipeline
  • Density Difference Estimation (with similar properties and features as Density Estimation)
  • Density Ratio Estimation (with similar properties and features as Least Squares Regression)
• Truncated combination grid functionality (#197)

Changed

• Add GitHub actions CI and moved Windows tests there (#252)

Fixed

• DataMatrix::resizeToSubMatrix off-by-two error (#232)
• Fixed various small build bugs discovered when creating/updating the SGpp Spack package (#229 #235 #241)

Removed

• Java support deprecated (#256)

[3.3.0] - 2020-03-24

Added

• New Combigrid module focused on usability (#176)
• Automatically run C++ and Python examples
• Travis packaging test
• A bunch of new spline based grid types (#143)
• Fuzzy framework in optimization module (#143)
• Debian java package (#215)
• Add conan recipe (#206)
• Grid coarsening added to the datamining pipeline (#200)

Changed

• Better decoupling of application and core functionality in the datamining pipeline
• Datamining pipeline examples reworked (#124)
• Libraries don't get installed into hardcoded "sgpp" subfolder (#222)

Fixed

• Fix C++ and Python examples
• Fix various optimization bugs
• Fix debian packging (#154)
• Fix python packaging (#209)

With this release, we have completed the transition to GitHub. Additionally, the updates include:

• Completed transition to Python 3 for the examples and bindings written in Python
• New functionality especially with respect to the data mining pipeline, e.g.,
  • improved interfaces
  • better I/O support
• Automatic support of GSL if available
• Travis support
• Fixed SCons configure on clean
• New logo :wink:

Finally, it's out there!

Please note that release v1.0.0 contains a major restructuring. Interfaces have been redesigned, factory methods restructured, and a lot of improvements have been done.

Important changes that might cause side-effects in codes using previous versions include:

• There are entirely new parameters for compilation. Please see scons -h for further information.
• Some of the Grid classes have been renamed. This affects especially the boundary-based grids. LinearBoundaryGrid is now the grid with the same discretization on the boundary as on the main axis.

Features of release v1.0.0:

• Automatic check for requirements in SCons for compilation
• Full module support
• Release of further and extended modules (optimization, pde, solver, datadriven, …)
• Extended parallelization support
• New types of basis functions
• More documentation, especially on adaptive refinement
• And much more…

v1.0.0 was originally on 26 Sep 2015.

• Compilation support for auto-detection of settings
• Extended use of DataVector from Python
• GridDataBase class

• Provides everything that is necessary to use spatially adaptive sparse grids for interpolation, optimization, …
• Several types of piecewise linear basis functions
  • Different boundary treatment
  • Modified boundary basis functions
• Grids (handling, creation, modification, …)
• Adaptivity
• Hash-based grid storage

v0.9.0 was originally released on 7 Jun 2010.

New features:

• Improved code quality
• New combigrid package
• OpenCL data mining
• Improved SCons support
• Support for installation (scons install)
• More unit tests
• New optimization method (CMA-ES)
• Some refactoring in sgpp::optimization (may break compatibility)
• Test problems for sgpp::optimization restructured

v1.1.0 was originally released on 24 Feb 2016.

As we have retructured (and significantly improved) several of the interfaces, we switched to a new, major version. As we don't provide backwards compatibility, we list here the most important changes:

• Massive interface change in sgpp::base from plain pointers to smart pointers and references (e.g., sgpp::base::Grid::createLinearGrid(), sgpp::base::Grid::getGenerator(), …)
• Consistent renaming of key functions in Grid, GridStorage, and GridIndex (e.g., sgpp::base::Grid::getDimension())
• Name change of global namespace from SGPP to sgpp
• Major redesign of SCons scripts, change of SCons parameters, better diagnostic output
• Add support for Clang 3.8
• Overall improvement of the code quality due to fixed Clang warnings
• Removal of single precision support and of double
• Removal of MSVC++ support
• More unit tests
• Small changes and bug fixes in code and documentation

v2.0.0 was originally released on 20 May 2016.

Important changes:

• We are modifying the preliminary combigrid module; therefore it is not included in this release
• Introduced C++11 features
  • smart pointers
• Completely restructured documentation of examples
• A lot more examples
• Improved build system
• Better unser instructions for build system
• More tests
• Clone method for Grids
• New learner functionality

v2.1.0 was originally released on 12 Aug 2016.

We joined GitHub for releases of SG++ starting with version 3.0.0. Please feel free to contribute, comment, extend, …

See our documentation for users and developers as well as the API and plenty of examples at sgpp.sparsegrids.org There we also provide a Debian package and Matlab binaries.

Below, we provide for your convenience:

• Debian package (C++ libraries only)
• Matlab mex-Files for Linux
• Matlab mex-Files for Windows

Important changes:

• We provide a new combigrid module with plenty of functionality!
  • An anisotropic implementation of the combination technique. This means that different 1D-grids and 1D - operators can be used for each dimension. This allows e.g. interpolating in one direction and integrating the interpolated function in another direction.
  • 1D grids: Uniform without boundary points (UniformPointDistribution), uniform with boundary points (UniformBoundaryPointDistribution), Clenshaw-Curtis (ClenshawCurtisDistribution, Chebyshev (ClenshawCurtisDistribution), (weighted), Leja (LejaPointDistribution), (weighted) L2-Leja (L2LejaPointDistribution)
  • Operators: Interpolation (PolynomialInterpolationEvaluator, BSplineInterpolationEvaluator, LinearInterpolationEvaluator), quadrature (PolynomialQuadratureEvaluator, BSplineQuadratureEvaluator), tensors (InterpolationCoefficientEvaluator)
  • Regular and adaptive (also parallel) level-generation: various level managers (RegularLevelManager, AveragingLevelManager) are available that predict the surplus of each subspace
  • Support for directly working on full-grid subspaces: enables to solve PDEs, regression problems, density estimation on each full-grid separately
  • Simple Python example and a C++ example to get started quickly
  • Serialization and deserialization of computed function values
  • Optimizations: function evaluations (but not the following computations) can be easily parallelized, interpolation at multiple points at once, operator coefficients and grid points are stored for recomputations at other evaluation points
  • Frequent use of abstract base classes such that extensions are easy to implement (e.g. new grids, operators and adaption schemes)
  • Various surrogate models for Uncertainty Quantification that are based on the Combination Technique are available:
    • B-Spline Collocation (collocation with B-splines in the hierarchical sparse grid space, BsplineStochasticCollocation)
    • Stochastic Collocation (global polynomial approximation with Legendre polynomials, PolynomialStochasticCollocation)
    • Polynomial Chaos Expansion (global polynomial approximation with orthogonal polynomials with respect to univariate weight functions, PolynomialChaosExpansion)
• A new plug-in-based data mining pipeline in datadriven, including:
  • Data input wrapper and support for CVS data files (CSVTools)
  • A regression-based fitter (FitterConfiguration)
  • A scorer wrapper for evaluation (Scorer)
  • New classification methods, such as a density-based learner with online-offline splitting (LearnerSGDEOnOff), including incomplete Cholesky factorization (DBMatDMSDenseIChol)
  • New adaptive refinement schemes, including joint refinement for multiple classes (MultipleClassRefinement)
• Support for ordered data sets
  • Pre-ordering of data sets on space-filling curves in Morton order to support data locality (MortonOrder)
• New grid spacings
  • General grid Stretching
  • Reactivation of BoundingBoxes
• Plenty of fixes and updates
  • Improved modularization: it is now easy to add own modules
  • Restructured examples, and a lot more of them
• … and much more!

v3.0.0 was originally released on 12 Mar 2018.