| AllkFN | Performs all-furthest-neighbors |
| AllkNN | Performs all-nearest-neighbors |
| AllNN | A computation class for dual-tree and naive all-nearest-neighbors |
| OPTPP::Appl_Data_NPSOL | |
| ArrayList< TElem > | A typical multi-purpose, resizable array, coded with an emphasis on speed |
| BinarySpaceTree< TBound, TDataset, TStatistic > | A binary space partitioning tree, such as KD or ball tree |
| OPTPP::BoolVector | BoolVector defines a vector of bools |
| optim::optpp::BoundConstraintTrait< Method, Objective, Applicable > | This trait is usefull for handling bound constraints |
| optim::optpp::BoundConstraintTrait< Method, Objective, true > | When the bound constraint is applicable then it updated the constraints accordingly |
| datanode | A node composed of a key-value pair, metadata, and child nodes |
| Dataset | Most generic dataset type |
| DatasetFeature | Metadata about a particular dataset feature (attribute) |
| DatasetInfo | Information describing a dataset and its features |
| DatasetScaler | A static class providing utilities for scaling the query and the reference datasets |
| DBallBound< TMetric, TPoint > | Ball bound that works in arbitrary metric spaces |
| DenseIntMap< TValue > | A dense grow-as-needed array that serves as an integer-keyed map |
| DHrectBound< t_pow > | Hyper-rectangle bound for an L-metric |
| DiscreteHMM | A wrapper class for HMM functionals in discrete case |
| DiskAllNN | A computation class for dual-tree and naive all-nearest-neighbors |
| DoneCondition | Reliable wait condition to signal readiness |
| DRange | Simple real-valued range |
| DualtreeKde< TKernelAux > | A computation class for dual-tree based kernel density estimation |
| DualtreeKdeCV< TKernelAux > | A computation class for dual-tree based kernel density estimation cross-validation |
| DualtreeVKde< TKernel > | A computation class for dual-tree based variable-bandwidth kernel density estimation |
| Empty | Empty object |
| EmptyStatistic< TDataset > | Empty statistic if you are not interested in storing statistics in your tree |
| EpanKernel | Multivariate Epanechnikov kernel |
| EpanKernelAux | Auxilairy computer class for Epanechnikov kernel |
| f77_complex | FORTRAN single-precision complex number |
| f77_doublecomplex | FORTRAN double-precision complex number |
| FarFieldExpansion< TKernelAux > | Far field expansion class in  expansion |
| FastICA | Class for running FastICA Algorithm |
| OPTPP::FDNLF1 | FDNLF1 is a derived class of NLP1 |
| OPTPP::FDNLF1APP | These classes and typedefs are used for Application Launching where an AppLauncher Object is also required so that the launcher specific data can be used for the function evaluation |
| FFTKde | A computation class for FFT based kernel density estimation |
| FGTKde | A computation class for FGT based kernel density estimation |
| fx_entry_doc | Documentation for an entry, for use with fx_module_doc |
| fx_module_doc | Documentation for an fx_module, used triply to provide usage information, to check input variables for correctness, and to enforce that programmers indeed do document their parameters |
| fx_submodule_doc | Documentation for a submodule, for use with fx_module_doc |
| GaussianHMM | A wrapper class for HMM functionals in single Gaussian case |
| GaussianKernel | Standard multivariate Gaussian kernel |
| KernelPCA::GaussianKernel | Example of a kernel |
| GaussianKernelAux | Auxiliary class for Gaussian kernel |
| GaussianKernelMultAux | Auxiliary class for multiplicative p^D expansion for Gaussian kernel |
| GaussianStarKernel | Standard multivariate Gaussian kernel |
| GeneralBinarySpaceTree< TBound, TDataset, TStatistic > | A binary space partitioning tree, such as KD or ball tree |
| GeneralCrossValidator< TLearner > | ALPHA VERSION, STILL UNDER CONSTRUCTION |
| GenMatrix< T > | Double-precision column-major matrix for use with LAPACK |
| GenVector< T > | Double-precision vector for use with LAPACK |
| GrainQueue< TGrain > | Simple difficulty-based work queue for easy parallelization |
| InfomaxICA | Infomax ICA |
| InversePowDistGradientKernelAux | The auxilary class for $r / ||r||^{}$ kernels using $O(D^p)$ expansion |
| InversePowDistKernelAux | The auxilary class for $1 / r^{}$ kernels using $O(D^p)$ expansion |
| KernelPCA | KernelPCA class is the main class that implements several spectral methods that are variances of Kernel PCA Most of them share an affinity (proximity) )matrix that is computed with the dual-tree all nearest algorithm |
| optim::optpp::LinearEqualityTrait< Method, Objective, Applicable > | Template<typename Method, typename Objective, bool Applicable> class LinearEqualityTrait; |
| optim::optpp::LinearInequalityTrait< Method, Objective, Applicable > | Template<typename Method, typename Objective, bool Applicable> class LinearInequalityTrait |
| LMetric< t_pow > | An L_p metric for vector spaces |
| LocalExpansion< TKernelAux > | Local expansion class |
| Lockable< TContained > | Mix-in to make a version of an existing object that can be locked |
| OPTPP::LSQNLF | LSQNLF is a derived class of NLP2 |
| MinHeap< TKey, TValue > | Priority queue implemented as a heap |
| MinMaxVal< TValue > | A value which is the min or max of multiple other values |
| MixtureofGaussianHMM | A wrapper class for HMM functionals in Mixture of Gaussion case |
| MoGEM | A Gaussian mixture model class |
| MoGL2E | A Gaussian mixture model class |
| MultFarFieldExpansion< TKernelAux > | Far field expansion class |
| MultLocalExpansion< TKernelAux > | Local expansion class |
| MultSeriesExpansionAux | Series expansion class for multiplicative kernel functions Precomputes constants for O(p^D) expansions |
| Mutex | Mutual exclusion lock to protect shared data |
| NaiveKde< TKernel > | A templatized class for computing the KDE naively |
| NaiveOrthoRangeSearch< T > | Naive orthogonal range search class |
| NelderMead | An optimizer using the Nelder Mead method, also known as the polytope or the simplex method |
| OPTPP::NLF0 | NLF0 is a derived class of NLP0, a nonlinear problem without analytic derivative information |
| OPTPP::NLF0APP | These classes and typedefs are used for Application Launching where an AppLauncher Object is also required so that the launcher specific data can be used for the function evaluation |
| OPTPP::NLF1 | NLF1 is a derived class of NLP1, a nonlinear problem with analytic first derivatives |
| OPTPP::NLF2 | NLF2 is a derived class of NLP2, a nonlinear problem with analytic first and second derivatives |
| OPTPP::NLP | NLP is a handle class for NLPBase |
| OPTPP::NLP0 | Base Class for NonLinear Programming Problem For NLP0 the only assumption on the objective function is that it be continuous |
| OPTPP::NLPBase | NLPBase is the Base Class for NonLinear Programming Problem |
| optim::optpp::NonLinearEqualityTrait< Method, Objective, Applicable > | Template<typename Method, typename Objective, bool Applicable> class NonLinearEqualityTrait; |
| optim::optpp::NonLinearInequalityTrait< Method, Objective, Applicable > | Template<typename Method, typename Objective, bool Applicable> class NonLinearInequalityTrait |
| OPTPP::OptBaNewton | OptBaNewton implements a bound constrained Newton method with a logarithmic barrier term |
| OPTPP::OptBaQNewton | OptBaQNewton implements a barrier Quasi-Newton method |
| OPTPP::OptBCEllipsoid | Bound Constrained Newton abstract data classes |
| OPTPP::OptBCFDNewton | OptBCFDNewton is a derived class of OptBCNewtonLike |
| OPTPP::OptBCNewton | OptBCNewton is a derived class of OptBCNewtonLike |
| OPTPP::OptBCNewton1Deriv | Bound constrained Newton class that will take either an NLP1 or NLP2 |
| OPTPP::OptBCNewton2Deriv | Bound constrained Newton class that requires an NLP2 |
| OPTPP::OptBCNewtonLike | Bound Constrained Newton abstract data classes OptBCNewtonLike OptBCNewton1Deriv OptBCNewton2Deriv OptBCNewtonLike provides common data and functionality for the OptBCQNewton, OptBCFDNewton, and OptBCNewton methods |
| OPTPP::OptBCQNewton | OptBCQNewton is a derived class of OptBCNewtonLike |
| OPTPP::OptCG | CG-Like Methods OptCG is a derived class from OptCGLike, which implements a nonlinear conjugate gradient method |
| OPTPP::OptCGLike | CG-Like Methods OptCG is a derived class from OptCGLike, which implements a nonlinear conjugate gradient method |
| OPTPP::OptConstrFDNewton | OptConstrFDNewton is a derived class of OptConstrNewtonLike |
| OPTPP::OptConstrNewton | OptConstrNewton is a derived class of OptConstrNewtonLike |
| OPTPP::OptConstrNewton1Deriv | Constrained Newton classes that will accept either an NLP1 or NLP2 |
| OPTPP::OptConstrNewton2Deriv | Constrained Newton classes that require an NLP2 |
| OPTPP::OptConstrNewtonLike | Constrained Newton abstract data classes OptConstrNewtonLike OptConstrNewton1Deriv OptConstrNewton2Deriv |
| OPTPP::OptConstrQNewton | OptConstrQNewton is a derived class of OptConstrNewtonLike |
| OPTPP::OptDHNIPS | OptDHNIPS is a derived class of OptNIPSLike |
| OPTPP::OptDirect | OptDirect is a derived class of OptimizeClass and the base class for direct search methods |
| OPTPP::OptFDNewton | OptFDNewton is a derived class of OptNewtonLike |
| OPTPP::OptFDNIPS | OptFDNIPS is a derived class of OptNIPSLike |
| optim::optpp::OptimizationTrait< Method > | Here we define a trait for the optimization, we need this trait to do the necessary initializations |
| optim::optpp::OptimizationTrait< OPTPP::OptFDNewton > | Trait specialization for Quasi-Newton with finite difference approximation of the Hessian |
| optim::optpp::OptimizationTrait< OPTPP::OptNewton > | Trait specialization for the Newton |
| optim::optpp::OptimizationTrait< OPTPP::OptNewtonLike > | Trait specialization for Newton method |
| optim::optpp::OptimizationTrait< OPTPP::OptQNewton > | Trait specialization for the Quasi-Newton BFGS |
| OPTPP::OptimizeClass | Opt is the Base Optimization Class All other Optimization classes are derived from this one |
| OPTPP::OptLBFGS | The Limited Memory BFGS Method for Large Scale Optimization |
| OPTPP::OptLBFGSLike | LBFGS-Like Methods OptLBFGS is a derived class of OptLBFGSLike that implements the LBFGS method of J |
| OPTPP::OptNewton | OptNewton is a derived class of OptNewtonLike |
| OPTPP::OptNewton1Deriv | Unconstrained Newton class that accepts either an NLP or NLP2 |
| OPTPP::OptNewton2Deriv | Unconstrained Newton class that requires an NLP2 |
| OPTPP::OptNewtonLike | OptNewtonLike is the base class for Newton Methods |
| OPTPP::OptNIPS | OptNIPS is a derived class of OptNIPSLike |
| OPTPP::OptNIPSLike | OptNIPSLike is a derived class of OptConstrNewtonLike |
| OPTPP::OptNPSOL | The interface to NPSOL software package for nonlinear programming |
| OPTPP::OptPDS | OptPDS is an implementation of a derivative-free algorithm for unconstrained optimization |
| OPTPP::OptppArray< T > | Simple array class |
| OPTPP::OptppExceptions | OptppExceptions is the base class for OptppMemoryError, OptppRangeError, OptppMathError, OptppDomainError, OptppZeroDivide |
| OPTPP::OptQNewton | OptQNewton is a derived class of OptNewtonLike |
| OPTPP::OptQNIPS | OptQNIPS is a derived class of OptNIPSLike |
| OrthoRangeSearch< T > | Faster orthogonal range search class using a tree |
| OPTPP::Problem | Class Problem is the parent for all the different types of solvers |
| QuasiNewton | An optimizer using the Quasi Newton method, also known as the variable metrics method |
| Queue< T > | A FIFO queue |
| RangeSet< TBoundary > | A set containing a union of [start,end) ranges that are automatically sorted and merged when possible |
| RecursiveMutex | Mutual exclusion lock to protect shared data, but can be locked and unlocked multiple times by the same thread without a deadlock |
| SeriesExpansionAux | Series expansion class |
| SimpleCrossValidator< TClassifier > | Cross-validator for simple classifiers, integrating tightly with FastExec |
| SimpleNaiveBayesClassifier | A classification class |
| SlabAllocator< item_size > | Fast memory allocator for identically sized chunks |
| SmallMatrix< t_rows, t_cols > | Low-overhead matrix if size is known at compile time |
| SmallVector< t_length > | Low-overhead vector if length is known at compile time |
| OPTPP::SmartPtr< T > | Howard Hinnant's reference counting handle class |
| ssm | Create a struct for the params |
| optim::optpp::StaticOptppOptimizer< Method, Objective, Constraint > | This is the core class for running optimization |
| stopwatch | Main timer structure |
| String | Non-stl string with some simple features |
| SVM< TKernel > | Class for SVM |
| SVMLinearKernel | Class for Linear Kernel |
| SVMRBFKernel | Class for Gaussian RBF Kernel |
| Task | Single start-to-finish task to be executed |
| template | , typename Objective, bool Applicable> class LinearEqualityTrait; |
| template | , typename Objective, bool Applicable> class LinearEqualityTrait; |
| TextLineReader | Helper for reading text files |
| TextTokenizer | Simple text tokenizer |
| TextWriter | Helper for writing text fo a file |
| Thread | Thread convenience wrapper |
| timestamp | Snapshot of both CPU and real time |
| OPTPP::TOLS | TOLS is the Base Class for Tolerances which will be used in the optimization methods |
| ValueCondition | Waits for a variable to take on a certain value |
| WaitCondition | Wait condition for alerting other threads of an action |
