Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 1234]

details
PolarizationIdentity
ROL
details
VectorClone
VectorCloneMap
VectorWorkspace
VectorKey
VectorStack
DynamicConstraint_CheckInterface
DynamicObjective_CheckInterface
MINRES
basic_nullstream
ROL
Constraint_SimOpt
SimConstraint
Constraint_TimeSimOpt
TypeB
AlgorithmState
Algorithm	Provides an interface to run bound constrained optimization algorithms
ColemanLiAlgorithm	Provides an interface to run the affine-scaling trust-region algorithm of Coleman and Li
GradientAlgorithm	Provides an interface to run the projected gradient algorithm
InteriorPointAlgorithm	Provides an interface to run the Moreau-Yosida algorithm
KelleySachsAlgorithm	Provides an interface to run the trust-region algorithm of Kelley and Sachs
LinMoreAlgorithm	Provides an interface to run the trust-region algorithm of Lin and More
LSecantBAlgorithm	Provides an interface to run the line-search algorithm of Byrd, Lu, Nocedal and Zhu (similar to L-BFGS-B)
MoreauYosidaAlgorithm	Provides an interface to run the Moreau-Yosida algorithm
NewtonKrylovAlgorithm	Provides an interface to run the projected secant algorithm
HessianPNK
PrecondPNK
PrimalDualActiveSetAlgorithm	Provides an interface to run the projected secant algorithm
HessianPDAS
HessianPDAS_Poly
PrecondPDAS
PrecondPDAS_Poly
QuasiNewtonAlgorithm	Provides an interface to run the projected secant algorithm
SpectralGradientAlgorithm	Provides an interface to run the spectral projected gradient algorithm
TrustRegionSPGAlgorithm	Provides an interface to run the trust-region algorithm
TypeE
AlgorithmState
Algorithm
AugmentedLagrangianAlgorithm	Provides an interface to run equality constrained optimization algorithms using Augmented Lagrangians
CompositeStepAlgorithm	Provides an interface to run equality constrained optimization algorithms using the Composite-Step Trust-Region Sequential Quadratic Programming (SQP) method
FletcherAlgorithm	Provides an interface to run equality constrained optimization algorithms using Fletcher's exact penalty
StabilizedLCLAlgorithm	Provides an interface to run equality constrained optimization algorithms using Stabilized LCL
TypeG
AlgorithmState
Algorithm	Provides an interface to run general constrained optimization algorithms
AugmentedLagrangianAlgorithm	Provides an interface to run general constrained optimization algorithms using Augmented Lagrangians
InteriorPointAlgorithm	Provides an interface to run the interior point algorithm
MoreauYosidaAlgorithm	Provides an interface to run the Moreau-Yosida algorithm
StabilizedLCLAlgorithm	Provides an interface to run general constrained optimization algorithms using Stabilized LCL
TypeP
AlgorithmState
Algorithm	Provides an interface to run optimization algorithms to minimize composite optimization problems f+phi
InexactNewtonAlgorithm	Provides an interface to run the inexact proximal Newton algorithm
NewtonObj
iPianoAlgorithm	Provides an interface to run the proximal gradient algorithm
ProxGradientAlgorithm	Provides an interface to run the proximal gradient algorithm
QuasiNewtonAlgorithm	Provides an interface to run the projected secant algorithm
SpectralGradientAlgorithm	Provides an interface to run the proximal gradient algorithm
TrustRegionAlgorithm	Provides an interface to run the trust-region algorithm
TypeU
AlgorithmState
Algorithm	Provides an interface to run unconstrained optimization algorithms
BundleAlgorithm	Provides an interface to run trust-bundle methods for unconstrained optimization algorithms
LineSearchAlgorithm	Provides an interface to run unconstrained line search algorithms
TrustRegionAlgorithm	Provides an interface to run trust-region methods for unconstrained optimization algorithms
TRUtils
InteriorPoint
PenalizedObjective
PrimalDualSymmetrizer
PrimalDualResidual	Express the Primal-Dual Interior Point gradient as an equality constraint
MeritFunction
StringList
Finite_Difference_Arrays
Exception
NotImplemented
ZOO
Objective_Beale	Beale's function
getBeale
Objective_BVP	The discrete boundary value problem
getBVP
Objective_Cantilever
Constraint_Cantilever
getCantilever
Objective_CantileverBeam
Constraint_CantileverBeam
getCantileverBeam
Objective_Cubic
Constraint_Cubic
getCubic
Objective_CylinderHead
Constraint_CylinderHead
getCylinderHead
Objective_DiodeCircuit	The diode circuit problem
Objective_FreudensteinRoth	Freudenstein and Roth's function
getFreudensteinRoth
Objective_HS1	W. Hock and K. Schittkowski 1st test function
getHS1
Objective_HS14	W. Hock and K. Schittkowski 14th test function
Constraint_HS14a
Constraint_HS14b
getHS14
Objective_HS2	W. Hock and K. Schittkowski 2nd test function
getHS2
Objective_HS21
Constraint_HS21
getHS21
Objective_HS24
Constraint_HS24
getHS24
Objective_HS25	W. Hock and K. Schittkowski 25th test function
getHS25
Objective_HS28	W. Hock and K. Schittkowski 28th test function
Constraint_HS28
getHS28
Objective_HS29
InequalityConstraint_HS29
getHS29
Objective_HS3	W. Hock and K. Schittkowski 3rd test function
getHS3
Objective_HS32
EqualityConstraint_HS32
InequalityConstraint_HS32
getHS32
Objective_HS38	W. Hock and K. Schittkowski 38th test function
getHS38
Objective_HS39	W. Hock and K. Schittkowski 39th test function
Constraint_HS39a
Constraint_HS39b
getHS39
Objective_HS4	W. Hock and K. Schittkowski 4th test function
getHS4
Objective_HS41	W. Hock and K. Schittkowski 41th test function
Constraint_HS41
getHS41
Objective_HS42	W. Hock and K. Schittkowski 42th test function
Constraint_HS42a
Constraint_HS42b
getHS42
Objective_HS45	W. Hock and K. Schittkowski 45th test function
getHS45
Objective_HS48	W. Hock and K. Schittkowski 48th test function
Constraint_HS48
getHS48
Objective_HS49	W. Hock and K. Schittkowski 49th test function
Constraint_HS49
getHS49
Objective_HS5	W. Hock and K. Schittkowski 5th test function
getHS5
Objective_HS50	W. Hock and K. Schittkowski 50th test function
Constraint_HS50
getHS50
Objective_HS51	W. Hock and K. Schittkowski 51th test function
Constraint_HS51
getHS51
Objective_HS52	W. Hock and K. Schittkowski 52nd test function
Constraint_HS52
getHS52
Objective_HS53	W. Hock and K. Schittkowski 53th test function
Constraint_HS53
getHS53
Objective_HS55	W. Hock and K. Schittkowski 55th test function
Constraint_HS55
getHS55
Objective_HS63	W. Hock and K. Schittkowski 63rd test function
Constraint_HS63a
Constraint_HS63b
getHS63
Objective_HS9	W. Hock and K. Schittkowski 9th test function
Constraint_HS9
getHS9
Objective_LeastSquares	Least squares function
getLeastSquares
Minimax1
getMinimax1
Minimax2
getMinimax2
Minimax3
getMinimax3
Objective_ParaboloidCircle	Objective function: f(x,y) = x^2 + y^2
Constraint_ParaboloidCircle	Constraint c(x,y) = (x-2)^2 + y^2 - 1
getParaboloidCircle
Objective_PoissonControl	Poisson distributed control
getPoissonControl
Objective_PoissonInversion	Poisson material inversion
getPoissonInversion
Objective_Powell	Powell's badly scaled function
getPowell
Objective_Quartic
Constraint_Quartic
getQuartic
Objective_Rosenbrock	Rosenbrock's function
getRosenbrock
Objective_SimpleEqConstrained	Objective function: f(x) = exp(x1*x2*x3*x4*x5) + 0.5*(x1^3+x2^3+1)^2
EqualityConstraint_SimpleEqConstrained	Equality constraints c_i(x) = 0, where: c1(x) = x1^2+x2^2+x3^2+x4^2+x5^2 - 10 c2(x) = x2*x3-5*x4*x5 c3(x) = x1^3 + x2^3 + 1
getSimpleEqConstrained
Objective_SumOfSquares	Sum of squares function
getSumOfSquares
Objective_Zakharov	Zakharov function
getZakharov
ElementwiseVector	Intermediate abstract class which does not require users implements plus, set, scale, axpy, norm, dot, or zero if they implement the three elementwise functions: applyUnary, applyBinary, and reduce
InactiveSet_PrimalVector	Defines the a Vector which has a diagonally scaled dot product that neglects active set elements Used to simplify Semi-smooth Newton method implementation
InactiveSet_DualVector	Defines the a Vector which has a diagonally scaled dot product that neglects active set elements Used to simplify Semi-smooth Newton method implementation
PartitionedVector	Defines the linear algebra of vector space on a generic partitioned vector
VectorFunctionCalls
ProfiledVector	By keeping a pointer to this in a derived Vector class, a tally of all methods is kept for profiling function calls
RieszPrimalVector
RieszDualVector
PrimalScaledStdVector	Provides the std::vector implementation of the ROL::Vector interface that handles scalings in the inner product. Also see ROL::DualScaledStdVector
DualScaledStdVector	Provides the std::vector implementation of the ROL::Vector interface that handles scalings in the inner product. Also see ROL::PrimalScaledStdVector
PrimalScaledVector	Provides the implementation of the ROL::Vector interface that handles scalings in the inner product. A more generic version of ROL::PrimalScaledStdVector
DualScaledVector	Provides the implementation of the ROL::Vector interface that handles scalings in the inner product. A more generic version of ROL::PrimalScaledStdVector
SingletonVector
StdArray	Provides the std::array implementation of the ROL::Vector interface
StdVector	Provides the ROL::Vector interface for scalar values, to be used, for example, with scalar constraints
Vector	Defines the linear algebra or vector space interface
Vector_SimOpt	Defines the linear algebra or vector space interface for simulation-based optimization
BoundConstraint	Provides the interface to apply upper and lower bound constraints
BoundConstraint_Partitioned	A composite composite BoundConstraint formed from bound constraints on subvectors of a PartitionedVector
Bounds	Provides the elementwise interface to apply upper and lower bound constraints
Active
BuildC
isGreater
LowerBinding
PruneBinding
SetZeroEntry
UpperBinding
AffineTransformConstraint	Compose a constraint operator with an affine transformation, i.e.,
BinaryConstraint	Implements an equality constraint function that evaluates to zero on the surface of a bounded parallelpiped and is positive in the interior
BoundsCheck
BoundToConstraint	Provides an implementation for bound constraints
ChainRuleConstraint	Defines a constaint formed through function composition \(c(x)=c_o(c_i(x))\)
Constraint	Defines the general constraint operator interface
Constraint_Partitioned	Has both inequality and equality constraints. Treat inequality constraint as equality with slack variable
ConstraintFromObjective	Creates a constraint from an objective function and a offset value
LinearConstraint	Defines the general affine constraint with the form \(c(x)=Ax+b\)
LowerBoundToConstraint	Provides an implementation for lower bound constraints
ReducedLinearConstraint	Reduce the input of a linear constraint based on the active set associated with a vector \(x\), i.e., let \(\mathcal{I}\) denote the inactive set associated with \(x\) and the bounds \(\ell\le u\), then
ScalarLinearConstraint	This equality constraint defines an affine hyperplane
SlacklessConstraint	This class strips out the slack variables from constraint evaluations to create the new constraint \( C(x,s) = c(x) \)
UpperBoundToConstraint	Provides an implementation for upper bound constraints
Constraint_DynamicState
DynamicConstraint	Defines the time-dependent constraint operator interface for simulation-based optimization
DynamicConstraintCheck
DynamicFunction	Provides update interface, casting and vector management to DynamicConstraint and DynamicObjective
DynamicObjective	Defines the time-dependent objective function interface for simulation-based optimization. Computes time-local contributions of value, gradient, Hessian-vector product etc to a larger composite objective defined over the simulation time. In contrast to other objective classes Objective_TimeSimOpt has a default implementation of value which returns zero, as time-dependent simulation based optimization problems may have an objective value which depends only on the final state of the system
DynamicObjectiveCheck
DynamicTrackingFEMObjective	Defines the time-local contribution to a quadratic tracking objective
DynamicTrackingObjective	Defines the time-local contribution to a quadratic tracking objective
NonlinearLeastSquaresObjective_Dynamic	Provides the interface to evaluate nonlinear least squares objective functions
ReducedDynamicObjective	Defines the reduced time-dependent objective function interface for simulation-based optimization
ReducedDynamicStationaryControlsObjectiveHook
ReducedDynamicStationaryControlsObjective	Defines the reduced time-dependent objective function interface for simulation-based optimization when the controls are stationary (i.e., not time-dependent)
SerialConstraint	Evaluates ROL::DynamicConstraint over a sequential set of time intervals
SerialFunction	Provides behavior common to SerialObjective as SerialConstaint
SerialObjective	Evaluates ROL::DynamicObjective over a sequential set of time intervals
TimeStamp	Contains local time step information
NonlinearLeastSquaresObjective	Provides the interface to evaluate nonlinear least squares objective functions
Objective_FSsolver
AffineTransformObjective	Compose an objective function with an affine transformation, i.e.,
BallIndicatorObjective	Provides the interface to evaluate the indicator function of norm constraints
ChainRuleObjective	Defines an objective of the form f(g(x)) where
CompositeObjective	Provides the interface to evaluate composite objective functions
l1Objective	Provides the interface to evaluate the weighted/shifted l1 objective function
ProjSymBnd
LinearCombinationObjective
LinearObjective	Provides the interface to evaluate linear objective functions
Objective	Provides the interface to evaluate objective functions
ObjectiveFromConstraint
QuadraticObjective	Provides the interface to evaluate quadratic objective functions
ScaledObjective
SlacklessObjective	This class strips out the slack variables from objective evaluations to create the new objective \( F(x,s) = f(x) \)
TypeBIndicatorObjective	Provides the interface to evaluate the indicator function of linear constraints
AugmentedSystemOperator	Apply the augmented system operator
AugmentedSystemPrecOperator	Implements a preconditioner for the augmented system
BlockOperator	Provides the interface to apply a block operator to a partitioned vector
BlockOperator2	Provides the interface to apply a 2x2 block operator to a partitioned vector
BlockOperator2Determinant	Provides the interface to the block determinant of a 2x2 block operator
BlockOperator2Diagonal	Provides the interface to apply a 2x2 block diagonal operator to a partitioned vector
BlockOperator2UnitLower	Provides the interface to apply a 2x2 block unit lower operator to a partitioned vector
BlockOperator2UnitUpper	Provides the interface to apply a 2x2 block unit upper operator to a partitioned vector
DiagonalOperator	Provides the interface to apply a diagonal operator which acts like elementwise multiplication when apply() is used and elementwise division when applyInverse() is used
DyadicOperator	Interface to apply a dyadic operator to a vector
HouseholderReflector	Provides the interface to create a Householder reflector operator, that when applied to a vector x, produces a vector parallel to y
IdentityOperator	Multiplication by unity
LinearOperator	Provides the interface to apply a linear operator
LinearOperatorFromConstraint	A simple wrapper which allows application of constraint Jacobians through the LinearOperator interface
LinearOperatorProduct	Provides the interface to the sequential application of linear operators.
LinearOperatorSum	Provides the interface to sum of linear operators applied to a vector
NullOperator	Multiplication by zero
NullSpaceOperator	Projects on to the null space of a linear constraint
RangeSpaceOperator	Projects on to the null space of a linear constraint
SchurComplement	Given a 2x2 block operator, perform the Schur reduction and return the decoupled system components
BrentsProjection
DaiFletcherProjection
DouglasRachfordProjection
DykstraProjection
PolyhedralProjection
RiddersProjection
SemismoothNewtonProjection
Jacobian
Precond
Problem
ProxObjective
ShiftedProxObjective
ZeroProxObjective
ConstraintData
ConstraintAssembler	Provides a wrapper for multiple constraints
ConstraintManager	Provides a wrapper for multiple constraints
NewConstraintManager	Provides a wrapper for multiple constraints
ObjectiveMMA	Provides the interface to to Method of Moving Asymptotes Objective function
ReduceLinearConstraint	Performs null-space transformation for reducible linear equality constraints
ScalarController
VectorController
BoundConstraint_SimOpt
CompositeConstraint_SimOpt	Defines a composite equality constraint operator interface for simulation-based optimization
CompositeObjective_SimOpt	Provides the interface to evaluate simulation-based composite objective functions
Constraint_SerialSimOpt	Unifies the constraint defined on a single time step that are defined through the Constraint_TimeSimOpt interface into a SimOpt constraint for all time. Primarily intended for use in testing the parallel-in-time implementation
Constraint_SimOpt	Defines the constraint operator interface for simulation-based optimization
Constraint_TimeSimOpt	Defines the time dependent constraint operator interface for simulation-based optimization
LinearCombinationObjective_SimOpt
LinearObjective_SimOpt	Provides the interface to evaluate linear objective functions
Objective_SerialSimOpt
Objective_SimOpt	Provides the interface to evaluate simulation-based objective functions
Objective_TimeSimOpt	Defines the time-dependent objective function interface for simulation-based optimization. Computes time-local contributions of value, gradient, Hessian-vector product etc to a larger composite objective defined over the simulation time. In contrast to other objective classes Objective_TimeSimOpt has a default implementation of value which returns zero, as time-dependent simulation based optimization problems may have an objective value which depends only on the final state of the system
Reduced_Constraint_SimOpt
Reduced_Objective_SimOpt
SimConstraint
Sketch	Provides an interface for randomized sketching
StdBoundConstraint
StdConstraint	Defines the equality constraint operator interface for StdVectors
StdLinearOperator	Provides the std::vector implementation to apply a linear operator, which is a std::vector representation of column-stacked matrix
StdObjective	Specializes the ROL::Objective interface for objective functions that operate on ROL::StdVector's
StdTridiagonalOperator	Provides the std::vector implementation to apply a linear operator, which encapsulates a tridiagonal matrix
Algorithm	Provides an interface to run optimization algorithms
OptimizationProblemCheckData
OptimizationProblem
OptimizationSolver	Provides a simplified interface for solving a wide range of optimization problems
Solver	Provides a simplified interface for solving a wide range of optimization problems
PQNObjective	Provides the interface to evaluate the quadratic quasi-Newton objective
AugmentedLagrangianObjective	Provides the interface to evaluate the augmented Lagrangian
FletcherObjectiveBase
FletcherObjectiveE
AugSystem
AugSystemPrecond
InteriorPointObjective
Mask
ModifiedDivide
ModifiedLogarithm
ModifiedReciprocal
MoreauYosidaObjective	Provides the interface to evaluate the Moreau-Yosida penalty function
ElasticLinearConstraint	Defines the general affine constraint with the form \(c(x)=g(x) + g'(x)s + u - v\)
ElasticObjective	Provides the interface to evaluate the elastic augmented Lagrangian
Bundle_U	Provides the interface for and implements a bundle
Bundle_U_AS	Provides the interface for and implements an active set bundle
Bundle_U_TT	Provides the interface for and implements a bundle. The semidefinite quadratic subproblem is solved using TT algorithm by Antonio Frangioni (1996)
DescentDirection_U	Provides the interface to compute unconstrained optimization steps for line search
Gradient_U	Provides the interface to compute optimization steps with the gradient descent method globalized using line search
Newton_U	Provides the interface to compute optimization steps with Newton's method globalized using line search
NewtonKrylov_U	Provides the interface to compute optimization steps with projected inexact Newton's method using line search
HessianNK
PrecondNK
NonlinearCG_U	Provides the interface to compute optimization steps with nonlinear CG
QuasiNewton_U	Provides the interface to compute optimization steps with a secant method
BackTracking_U	Implements a simple back tracking line search
CubicInterp_U	Implements cubic interpolation back tracking line search
IterationScaling_U	Provides an implementation of iteration scaled line search
LineSearch_U	Provides interface for and implements line searches
PathBasedTargetLevel_U	Provides an implementation of path-based target leve line search
ScalarMinimizationLineSearch_U	Implements line search methods that attempt to minimize the scalar function \(\phi(t) := f(x+ts)\)
Phi
StatusTest
ConicApproximationModel
CauchyPoint_U	Provides interface for the Cauchy point trust-region subproblem solver
DogLeg_U	Provides interface for dog leg trust-region subproblem solver
DoubleDogLeg_U	Provides interface for the double dog leg trust-region subproblem solver
SPGTrustRegion_U	Provides interface for truncated CG trust-region subproblem solver
TruncatedCG_U	Provides interface for truncated CG trust-region subproblem solver
TrustRegion_U	Provides interface for and implements trust-region subproblem solvers
TrustRegionModel_U	Provides the interface to evaluate trust-region model functions
BundleStatusTest
CombinedStatusTest	Provides an interface to check two status tests of optimization algorithms
ConstraintStatusTest	Provides an interface to check status of optimization algorithms for problems with equality constraints
FletcherStatusTest	Provides an interface to check status of optimization algorithms for problems with equality constraints
StatusTest	Provides an interface to check status of optimization algorithms
StatusTestFactory
AugmentedLagrangian	Provides the interface to evaluate the augmented Lagrangian
AugmentedLagrangian_SimOpt	Provides the interface to evaluate the SimOpt augmented Lagrangian
QuadraticPenalty	Provides the interface to evaluate the quadratic constraint penalty
QuadraticPenalty_SimOpt	Provides the interface to evaluate the quadratic SimOpt constraint penalty
Reduced_AugmentedLagrangian_SimOpt	Provides the interface to evaluate the reduced SimOpt augmented Lagrangian
Bundle	Provides the interface for and implements a bundle
Bundle_AS	Provides the interface for and implements an active set bundle
Bundle_TT	Provides the interface for and implements a bundle. The semidefinite quadratic subproblem is solved using TT algorithm by Antonio Frangioni (1996)
BoundFletcher
AugSystemNonSym
AugSystemPrecond
AugSystemSym
DiffLower
DiffUpper
FormDQ
FormQ
Fletcher
AugSystem
AugSystemPrecond
FletcherBase
InteriorPointPenalty	Provides the interface to evaluate the Interior Pointy log barrier penalty function with upper and lower bounds on some elements
Mask
ModifiedDivide
ModifiedLogarithm
ModifiedReciprocal
LogBarrierObjective	Log barrier objective for interior point methods
ObjectiveFromBoundConstraint	Create a penalty objective from upper and lower bound vectors
PrimalDualInteriorPointBlock11
PrimalDualInteriorPointBlock12
PrimalDualInteriorPointBlock21
PrimalDualInteriorPointBlock22
PrimalDualInteriorPointResidual	Symmetrized form of the KKT operator for the Type-EB problem with equality and bound multipliers
InFill
SafeDivide
SetZeros
PrimalDualSystemStep	Provides the interface to compute approximate solutions to 2x2 block systems arising from primal-dual interior point methods
BiCGSTAB
ConjugateGradients	Provides definitions of the Conjugate Gradient solver
ConjugateResiduals	Provides definition of the Conjugate Residual solver
GMRES	Preconditioned GMRES solver
Krylov	Provides definitions for Krylov solvers
Lanczos	Interface for computing the Lanczos vectors and approximate solutions to symmetric indefinite linear systems
BackTracking	Implements a simple back tracking line search
Bisection	Implements a bisection line search
Brents	Implements a Brent's method line search
testFunction
CubicInterp	Implements cubic interpolation back tracking line search
GoldenSection	Implements a golden section line search
IterationScaling	Provides an implementation of iteration scaled line search
LineSearch	Provides interface for and implements line searches
PathBasedTargetLevel	Provides an implementation of path-based target leve line search
ScalarMinimizationLineSearch	Implements line search methods that attempt to minimize the scalar function \(\phi(t) := f(x+ts)\)
LineSearchStatusTest
Phi
MoreauYosidaPenalty	Provides the interface to evaluate the Moreau-Yosida penalty function
MoreauYosidaPenaltyStep	Implements the computation of optimization steps using Moreau-Yosida regularized bound constraints
InteriorPointStep
AugmentedLagrangianStep	Provides the interface to compute augmented Lagrangian steps
BundleStep	Provides the interface to compute bundle trust-region steps
CompositeStep	Implements the computation of optimization steps with composite-step trust-region methods
FletcherStep	Provides the interface to compute Fletcher steps
GradientStep	Provides the interface to compute optimization steps with the gradient descent method globalized using line search
LineSearchStep	Provides the interface to compute optimization steps with line search
NewtonKrylovStep	Provides the interface to compute optimization steps with projected inexact Newton's method using line search
HessianNK
PrecondNK
NewtonStep	Provides the interface to compute optimization steps with Newton's method globalized using line search
NonlinearCGStep	Provides the interface to compute optimization steps with nonlinear CG
PrimalDualActiveSetStep	Implements the computation of optimization steps with the Newton primal-dual active set method
HessianPD
PrecondPD
ProjectedNewtonKrylovStep	Provides the interface to compute optimization steps with projected inexact ProjectedNewton's method using line search
HessianPNK
PrecondPNK
ProjectedNewtonStep	Provides the interface to compute optimization steps with projected Newton's method using line search
ProjectedSecantStep	Provides the interface to compute optimization steps with projected secant method using line search
SecantStep	Provides the interface to compute optimization steps with a secant method
Step	Provides the interface to compute optimization steps
StepFactory
TrustRegionStep	Provides the interface to compute optimization steps with trust regions
lBFGS	Provides definitions for limited-memory BFGS operators
lDFP	Provides definitions for limited-memory DFP operators
lSR1	Provides definitions for limited-memory SR1 operators
SecantState
Secant	Provides interface for and implements limited-memory secant operators
ColemanLiModel	Provides the interface to evaluate interior trust-region model functions from the Coleman-Li bound constrained trust-region algorithm
DogLeg	Provides interface for dog leg trust-region subproblem solver
DoubleDogLeg	Provides interface for the double dog leg trust-region subproblem solver
KelleySachsModel	Provides the interface to evaluate projected trust-region model functions from the Kelley-Sachs bound constrained trust-region algorithm
LowerBinding
PruneBinding
PruneNonbinding
UpperBinding
LinMore	Provides interface for truncated CG trust-region subproblem solver
LowerBreakPoint
PositiveMax
PositiveMin
UpperBreakPoint
LinMoreModel	Provides the interface to evaluate projected trust-region model functions from the Kelley-Sachs bound constrained trust-region algorithm
TruncatedCG	Provides interface for truncated CG trust-region subproblem solver
TrustRegion	Provides interface for and implements trust-region subproblem solvers
TrustRegionModel	Provides the interface to evaluate trust-region model functions
PD_BPOE
PD_CVaR
PD_HMCR2
PD_MeanSemiDeviation
PD_MeanSemiDeviationFromTarget
PD_RandVarFunctional
LinearRegression	Provides the interface to construct linear regression problem
PrimalDualRisk
ProgressiveHedging	Provides the interface to solve a stochastic program using progressive hedging
StochasticProblem
Arcsine
Beta
Cauchy
Dirac
Distribution
Exponential
Gamma
Gaussian
Gumbel
Kumaraswamy
Laplace
Logistic
Parabolic
RaisedCosine
Smale
Triangle
TruncatedExponential
TruncatedGaussian
Uniform
ExpectationQuad	Provides a general interface for risk and error measures generated through the expectation risk quadrangle
GenMoreauYosidaCVaR
LogExponentialQuadrangle	Provides an interface for the entropic risk using the expectation risk quadrangle
LogQuantileQuadrangle	Provides an interface for the conditioanl entropic risk using the expectation risk quadrangle
MeanVarianceQuadrangle	Provides an interface for the mean plus variance risk measure using the expectation risk quadrangle
MoreauYosidaCVaR	Provides an interface for a smooth approximation of the conditional value-at-risk
QuantileQuadrangle	Provides an interface for a convex combination of the expected value and the conditional value-at-risk using the expectation risk quadrangle
SmoothedWorstCaseQuadrangle	Provides an interface for a smoothed version of the worst-case scenario risk measure using the expectation risk quadrangle
TruncatedMeanQuadrangle
PH_bPOEObjective	Provides the interface for the progressive hedging probability objective
PH_DeviationObjective	Provides the interface for the progressive hedging deviation objective
PH_ErrorObjective	Provides the interface for the progressive hedging error objective
PH_Objective	Provides the interface for the progressive hedging objective
PH_ProbObjective	Provides the interface for the progressive hedging probability objective
PH_RegretObjective	Provides the interface for the progressive hedging regret objective
PH_RiskObjective	Provides the interface for the progressive hedging risk objective
ExpectationQuadDeviation
ExpectationQuadError	Provides a general interface for error measures generated through the expectation risk quadrangle
BPOE	Provides the implementation of the buffered probability of exceedance
SmoothedPOE	Provides the implementation of the smoothed probability of exceedance
ExpectationQuadRegret	Provides a general interface for regret measures generated through the expectation risk quadrangle
Chi2Divergence	Provides an interface for the chi-squared-divergence distributionally robust expectation
FDivergence	Provides a general interface for the F-divergence distributionally robust expectation
CoherentEntropicRisk	Provides the interface for the coherent entropic risk measure
ConvexCombinationRiskMeasure	Provides an interface for a convex combination of risk measures
CVaR	Provides an interface for a convex combination of the expected value and the conditional value-at-risk
EntropicRisk	Provides an interface for the entropic risk
ExpectationQuadRisk
HMCR	Provides an interface for a convex combination of the expected value and the higher moment coherent risk measure
KLDivergence	Provides an interface for the Kullback-Leibler distributionally robust expectation
MeanDeviation	Provides an interface for the mean plus a sum of arbitrary order deviations
MeanDeviationFromTarget	Provides an interface for the mean plus a sum of arbitrary order deviations from targets
MeanSemiDeviation	Provides an interface for the mean plus upper semideviation of order 1
MeanSemiDeviationFromTarget
MeanVariance	Provides an interface for the mean plus a sum of arbitrary order variances
MeanVarianceFromTarget	Provides an interface for the mean plus a sum of arbitrary order variances from targets
MixedCVaR	Provides an interface for a convex combination of conditional value-at-risks
QuantileRadius
ChebyshevSpectral	Provides an interface for the Chebyshev-Spectral risk measure
SecondOrderCVaR	Provides an interface for the risk measure associated with the super quantile quadrangle
SpectralRisk	Provides an interface for spectral risk measures
RandVarFunctional	Provides the interface to implement any functional that maps a random variable to a (extended) real number
SampledScalar
SampledVector
StochasticConstraint
StochasticObjective
AbsoluteValue
AlmostSureConstraint
MeanValueConstraint
MeanValueObjective
PlusFunction
PositiveFunction
RegressionError	Provides the interface to evaluate linear regression error
RiskBoundConstraint
RiskLessConstraint
RiskLessObjective
RiskMeasure	Provides the interface to implement risk measures
RiskNeutralConstraint
RiskNeutralObjective
SimulatedBoundConstraint	A BoundConstraint formed from a single bound constraint replacated according to a SampleGenerator
SimulatedConstraint
SimulatedObjective
SimulatedObjectiveCVaR
BatchManager
MonteCarloGenerator
SampleGenerator
SROMGenerator
UserInputGenerator
PrimalAtomVector
DualAtomVector
AtomVector	Provides the std::vector implementation of the ROL::Vector interface
BatchStdVector	Provides the std::vector implementation of the ROL::Vector interface
CDFObjective
MomentObjective
PointwiseCDFObjective
PrimalProbabilityVector
DualProbabilityVector
ProbabilityVector	Provides the std::vector implementation of the ROL::Vector interface
SROMVector	Provides the std::vector implementation of the ROL::Vector interface
PH_StatusTest	Provides an interface to check status of the progressive hedging algorithm
RiskVector
PrimalSimulatedVector
DualSimulatedVector
SimulatedVector	Defines the linear algebra of a vector space on a generic partitioned vector where the individual vectors are distributed in batches defined by ROL::BatchManager. This is a batch-distributed version of ROL::PartitionedVector
ProjectedObjective
ScalarTraits_Magnitude
ScalarTraits_Magnitude< std::complex< Real > >
ScalarTraits
AlgorithmState	State for algorithm class. Will be used for restarts
StepState	State for step class. Will be used for restarts
removeSpecialCharacters
TypeCaster
TypeCaster< Real, std::complex< Real > >
TypeCaster< double, float >
TestProblem
VectorClone	Container for wrapping a reusable cloned vector. Declaring an object of this type as a class member variable will decrease the number of clones needed as memory need only be allocated once in the lifetime of the host object. Verifies that member and argument types and dimensions agree when called
VectorCloneMap	Container for wrapping a collection of uniquely-named reusable cloned vectors, which in are stored in a map. Uses string-valued ids for keys by default
VectorWorkspace	Provides a "smart" cloning manager to be used a member variable in a class and called in the member function of the same class
WrappedVector	Provides an interface layer which encapulates a pointer to a ROL::Vector and has the default behavior of calling its member Ptr<Vector> object. Makes creating derived classes with this idiom simpler as they need only override the methods where the desired implementation differs from the member Ptr<Vector>. For example, vectors which have a diagonal scaling that defines their inner product and dual spaces can derive from this class need overload only the methods basis, clone, dual, and dot
StdLinearOperatorFactory	Creates StdLinearOperator objects which wrap random matrices of the desired size and property
PrimalInteriorPointObjective	Provides the interface to evaluate the Interior Pointy log barrier penalty function with upper and lower bounds on some elements
SemismoothNewtonDualModel	Implements the dual variable model function for a semismooth Newton step
PenalizedObjective	Adds barrier term to generic objective
PrimalDualInteriorPointReducedResidual	Reduced form of the Primal Dual Interior Point residual and the action of its Jacobian
MINRES	Implements the MINRES algorithm for solving symmetric indefinite systems
BoundConstraint_BurgersControl
Bounds	Provides the elementwise interface to apply upper and lower bound constraints
BurgersFEM
CLExactModel
CLTestObjective
con2d
ConDualStdVector
ConicApproximationModel	Provides the interface to evaluate conic approximation function
ConStdVector
constraint1
constraint2
Constraint_BurgersControl
DiffusionConstraint
DiffusionObjective
Example_Objective	Objective function: \[f(x) = exp(x_1 x_2 x_3 x_4 x_5) + \frac{1}{2}*(x_1^3+x_2^3+1)^2 \]
FEM
FiniteDifference
FunctionZakharov
H1BoundConstraint
H1VectorBatchManager
H1VectorDual
H1VectorPrimal
Identity
IdentityOperator
InnerConstraint
InnerProductMatrix
InnerProductMatrixSolver	This class adds a solve method
L2BoundConstraint
L2VectorBatchManager
L2VectorDual
L2VectorPrimal
Lagrange
NodalBasis
Normalization_Constraint
NullObjective
Objective_BurgersControl
Objective_GrossPitaevskii
Objective_PoissonInversion
ObjectiveFromConstraint	Form an objective function from a ROL::Constraint and a vector in the dual constraint space \(\lambda\in \mathcal{C}^\ast\)
ObjectiveFunctionTest06
ObjectiveFunctionTest07_1
ObjectiveFunctionTest07_2
ObjectiveFunctionTest07_scalarize
ObjectiveFunctionTest08_1
ObjectiveFunctionTest08_2
ObjectiveFunctionTest08_scalarize
OptDualStdVector
OptStdVector
OuterConstraint
redConstraint
StatusTest_PDAS
TestMulti
TestSingle
TridiagonalToeplitzOperator
valConstraint
Zakharov
Zakharov_Sacado_Objective