.. _releasenotes-50:

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Version 5.0 (2009/12/21)
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**Highlights**

- DAKOTA changes license to the GNU Lesser General Public License to enable library mode users, incorporating license changes for several TPLs
- All new JAGUAR 2.0 graphical user interface for creating, editing, and running DAKOTA input files (BSD-like license)
- Additional discrete range and discrete set types within design, uncertain, and state variables. New types supported by parameter studies, nondeterministic sampling, JEGA, and COLINY.
- Anisotropic sparse grids, numerically-generated orthogonal polynomials, and improved expansion tailoring for stochastic expansion UQ methods
- New methods for epistemic and mixed aleatory-epistemic uncertainty quantification: local/global interval estimation and local/global evidence.
- Creation and management of evaluation working directories
- SNL: DAKOTA modules on compute clusters (module avail dakota)

**Usability/Core Execution**

- JAGUAR 2.0 graphical user interface, based on Eclipse Workbench, with text- and graphical-based editing, templates, sensitivity analysis wizard, and error checking. Available via separate download after registering for DAKOTA download.
- Capability to specify working directories, template directories, and lightweight linking for system and fork interfaces.
- DAKOTA input files can use Matlab-style sequence notation (L:S:U and beyond) to specify variable ranges; improved tolerance for whitespace in input files
- Improved DAKOTA + application parallelism examples; new asynchronous local evaluation static scheduling for improved parallel tiling on clusters.
- Pre-run (with optional variables output) and post-run (with variables/responses input) modes supported by sampling, paramter study, and DACE methods
- Examples of Matlab and Python interfaces
- Automated cleanup of DAKOTA temporary files on unexpected exit

**Variables**

- Refactor of Variables and Constraints hierarchies to manage continuous, discrete integer, and discrete real domains among design, aleatory uncertain, epistemic uncertain, and state types.

  - Additional discrete design variable types: discrete design integer range, discrete design integer set, discrete design real set
  - New discrete uncertain distibutions: poisson, binomial, negative binomial, geometric, hypergeometric
  - Additional discrete state variable types: discrete state integer range, discrete state integer set, discrete state real set

- Updates to specification of continuous and discrete histograms
- Alternate specification for lognormal using lambdas and zetas

**Methods (general)**

- New capability to perform parameter studies and sampling over discrete variables. New discrete variable types also supported in JEGA and COLINY.
- DACE and parameter study classes (DDACE, FSUDACE, and multi-dimensional parameter study) can have correlations calculated and printed in addition to sampling methods
- Improved accuracy and robustness in correlation computations
- PSUADE: use updated (Campolongo 2007) sample generation scheme to improve space-filling properties
- Improved EGO convergence controls (based on nearest neighbor)
- Beta capabilities:

  - wrapper class for LANL's GPMSA code (Bayesian calibration)
  - importance sampling capability
  - nonlinear conjugate gradient optimization solver using Trilinos vector-matrix utilities
  
- Bug fixes:

  - hang in DDACE orthogonal array LHS
  - OPT++ NIP methods not respecting bound constraints

- New example problems for multifidelity OUU (MVFOSM as low fidelity UQ and stochastic expansion as high fidelity UQ), epistemic UQ, mixed aleatory-epistemic UQ, discrete sampling and parameter studies, etc.

**Uncertainty Quantification (UQ) Capabilities:**

- Stochastic expansions (polynomial chaos expansion (PCE) and stochastic collocation (SC)):

  - Simplified controls for PCE: expansion formulation now inferred or estimated from quadrature_order or sparse_grid_level specifications. Automatic expansion tailoring minimizes performance loss due to poor expansion/integration synchronization.
  - Addition of numerically-generated orthogonal polynomials for generating optimal basis for non-Askey distribution types (uses Gauss-Wigert or discretized Stieltjes procedures for polynomial recursion coefficients in combination with Golub-Welsch for Gauss point/weight computation)
  - Addition of analytic variance-based decomposition for global sensitivity analysis using method of Sobol indices
  - Addition of analytic covariance among multiple response functions.
  - Addition of anisotropic Smolyak sparse grids, with user-supplied dimension preference.
  - Revision of Gaussian quadrature rules within sparse grids to use linear growth, providing finer grain control and more uniform integrand coverage.
  - PCE customizations for sparse grids: exclusive usage of linear growth rules (no Clenshaw-Curtis or Gauss-Patterson), total-order expansions for isotropic, custom expansion for anisotropic.
  - Addition of Askey and Wiener basis polynomial over-rides.
  - Modified variable correlation logic to revert to Wiener expansions for de-correlation as needed on a per-variable basis.
  - all_variables mode now includes epistemic uncertain variables (previously design and state only) using a Legendre basis.

- Epistemic UQ methods:

  - Refactor of NonDInterval hierarchy to generalize and incorporate new methods.
  
    - Evidence: new global (LHS or EGO) and local (SQP or NIP) methods to calculate belief and plausibility in evidence theory calculations. Removed dependence on legacy Fortran package.
    - Interval estimation: new global (LHS or EGO) and local (SQP or NIP) methods to calculate response output intervals.

  - Extension of active Variables/Constraints views to support aleatory, epistemic, or aggregated aleatory-epistemic uncertain views.

- Mixed aleatory-epistemic UQ methods:

  - New nested approaches (e.g. second-order probability, mixed evidence) where the outer loop (e.g. interval estimation or evidence) can leverage analytic moments and their sensitivities with respect to epistemic parameters.

**Framework Enhancements:**

- Eliminated dependence on GSL in favor of Boost (to eliminate GNU GPL dependency)
- Fully deployed Teuchos for all numerical data types. Boost and STL are used for all bookkeeping types.
- Better out-of-source (VPATH) builds and documentation, including management of Boost and Teuchos
- Switched from RNUM2 to Boost MT19937 random number generator for longer period, with run-time selection option
- Deployed Boost multi-index containers to evaluation queue management, supporting multiple indexing options for optimized lookups.
- Lightweight active/inactive data views implemented with Teuchos and Boost multi-array to eliminate deep data copies.
- Updates to third-party libraries: JEGA, PSUADE, AMPL, Boost, Pecos, Teuchos
- Minimal data mode for variables and responses omits labels, types, ids in some contexts for better scalability
- Unified bounds checking for debugging and reduced overhead in optimized builds

**Miscellaneous:**

- Binary distributions for Mac OS X and Cygwin, including library dependencies
- Better detection of MPICH2 and shmem communicators
- Automatic syncronization of DAKOTA input specification help docs to JAGUAR
- Surfpack: improved random seed management for repeatability.
- Better handling of numerical comparisons on 32-bit
- Finite differencing honors variable bounds; more efficient finite difference Hessians
- PSUADE generally available under LGPL
- Surfpack and LHS relicensed under LGPL
- DLL API improvements for re-entry, error handling, get/set options
- ModelCenter, MATLAB, and Python interface updates to latest API
- Updates to Matlab memory management
- HTTP-based usage tracking (disabled by default; currently used Sandia internally only)
- Windows: use spawn as alternative to fork
