| Modifier and Type | Method and Description |
|---|---|
static Matrix |
MatrixFactory.foreachColumn(Matrix matrix,
RealVectorFunction f)
Constructs a new matrix in which each column is the result of applying a real vector function
on each column vector of an input matrix.
|
static Matrix |
MatrixFactory.foreachRow(Matrix A,
RealVectorFunction f)
Constructs a new matrix in which each row is the result of applying a real vector function on
each row vector of an input matrix.
|
| Modifier and Type | Method and Description |
|---|---|
static Vector[] |
VectorFactory.foreachVector(Collection<Vector> vectors,
RealVectorFunction f)
Applies a real vector function on each input vector.
|
static Vector[] |
VectorFactory.foreachVector(Vector[] vectors,
RealVectorFunction f)
Applies a real vector function on each input vector.
|
| Modifier and Type | Method and Description |
|---|---|
static DerivativeFunction |
ODE1stOrder.convertToDerivativeFunction(RealVectorFunction F,
int dimension)
Converts the given vector function to a first order derivative function.
|
| Constructor and Description |
|---|
ODE1stOrder(RealVectorFunction F,
Vector y0,
double x0,
double x1)
Constructs a first order ODE with the given vector-valued function and its initial values.
|
ODE1stOrderWith2ndDerivative(RealVectorFunction dy,
RealVectorFunction ddy,
Vector y0,
double x0,
double x1)
Constructs a first order ODE with initial values.
|
| Modifier and Type | Method and Description |
|---|---|
RealVectorFunction |
C1.g()
Get the gradient function, g, of a real valued function f.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GradientFunction
The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.
|
| Constructor and Description |
|---|
Jacobian(RealVectorFunction f,
Vector x)
Construct the Jacobian matrix for a multivariate function f at point x.
|
JacobianFunction(RealVectorFunction f)
Construct the Jacobian function of a real scalar function f.
|
| Constructor and Description |
|---|
R1Projection(RealVectorFunction f,
int dimension)
Construct a \(R^n \rightarrow R\) projection from a \(R^n \rightarrow R^m\) function f.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractR1RnFunction
This is a function that takes one real argument and outputs one vector value.
|
class |
AbstractRealVectorFunction
This abstract implementation implements
Function.dimensionOfDomain() and
Function.dimensionOfRange() by returning the input arguments at constructor. |
class |
RealVectorSubFunction
This constructs a
RealVectorFunction from another RealVectorFunction by
restricting/fixing the values of a subset of variables. |
| Constructor and Description |
|---|
RealVectorSubFunction(RealVectorFunction f,
Map<Integer,Double> fixing)
Construct a vector-valued sub-function.
|
| Modifier and Type | Method and Description |
|---|---|
Vector |
NewtonSystemRoot.solve(RealVectorFunction f,
Vector guess)
Searches for a root, x such that f(x) = 0.
|
| Modifier and Type | Method and Description |
|---|---|
SQPASVariation |
SQPActiveSetMinimizer.VariationFactory.newVariation(RealScalarFunction f,
RealVectorFunction g,
EqualityConstraints equal,
GreaterThanConstraints greater)
Construct a new instance of
SQPASVariation for an SQP
problem. |
void |
SQPASVariation1.set(RealScalarFunction f,
RealVectorFunction g,
EqualityConstraints equal,
GreaterThanConstraints greater)
Associate this variation to a particular general constrained minimization
problem.
|
IterativeSolution<Vector> |
SQPActiveSetOnlyInequalityConstraintMinimizer.solve(RealScalarFunction f,
RealVectorFunction g,
GreaterThanConstraints greater)
Minimize a function subject to only inequality constraints.
|
| Constructor and Description |
|---|
Solution(RealScalarFunction f,
RealVectorFunction g,
EqualityConstraints equal,
GreaterThanConstraints greater) |
| Modifier and Type | Method and Description |
|---|---|
RealVectorFunction |
MinMaxProblem.gradient(T omega)
g(x, ω) = ∇|e(x, ω)| is the gradient function of the absolute error, |e(x, ω)|, for a given ω.
|
| Modifier and Type | Method and Description |
|---|---|
IterativeSolution<Vector> |
GaussNewtonMinimizer.solve(RealVectorFunction vf)
Solve the minimization problem to minimize F = vf' * vf.
|
IterativeSolution<Vector> |
GaussNewtonMinimizer.solve(RealVectorFunction vf,
RntoMatrix J)
Solve the minimization problem to minimize F = vf' * vf.
|
| Modifier and Type | Method and Description |
|---|---|
RealVectorFunction |
C2OptimProblemImpl.g() |
| Constructor and Description |
|---|
C2OptimProblemImpl(RealScalarFunction f,
RealVectorFunction g)
Construct an optimization problem with an objective function.
|
C2OptimProblemImpl(RealScalarFunction f,
RealVectorFunction g,
RntoMatrix H)
Construct an optimization problem with an objective function.
|
| Constructor and Description |
|---|
MultivariateExponentialFamily(RealScalarFunction h,
RealVectorFunction eta,
RealVectorFunction T,
RealScalarFunction A)
Construct a factory to construct probability distribution in the exponential family of this
form.
\[
f_X(x|\boldsymbol \theta) = h(x) \exp\Big(\boldsymbol\eta({\boldsymbol \theta}) \cdot
\mathbf{T}(x) - A({\boldsymbol \theta}) \Big)
\]
|
| Constructor and Description |
|---|
ExponentialFamily(UnivariateRealFunction h,
RealVectorFunction eta,
AbstractR1RnFunction T,
RealScalarFunction A)
Construct a factory to construct probability distribution in the exponential family of this
form.
\[
f_X(x|\boldsymbol \theta) = h(x) \exp\Big(\boldsymbol\eta({\boldsymbol \theta}) \cdot
\mathbf{T}(x) - A({\boldsymbol \theta}) \Big)
\]
|
| Modifier and Type | Method and Description |
|---|---|
static RealVectorFunction |
AbstractHybridMCMC.dUdx(RealVectorFunction dLogF)
Gets the derivative of the potential function, given the derivative of the log density.
|
| Modifier and Type | Method and Description |
|---|---|
static RealVectorFunction |
AbstractHybridMCMC.dUdx(RealVectorFunction dLogF)
Gets the derivative of the potential function, given the derivative of the log density.
|
| Constructor and Description |
|---|
HybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
Vector initialState,
RandomLongGenerator rlg)
Constructs a new instance with the given parameters.
|
LeapFrogging(RealVectorFunction dU,
Vector m,
Vector x,
Vector p,
double dt)
Constructs a new instance with the given parameters.
|
MultipointHybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
int M,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with equal weights to the M configurations.
|
MultipointHybridMCMC(RealScalarFunction logF,
RealVectorFunction dLogF,
Vector m,
double dt,
int L,
int M,
Vector w,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with the given parameters.
|
| Constructor and Description |
|---|
Metropolis(RealScalarFunction logf,
RealVectorFunction proposalFunction,
Vector initialState,
RandomLongGenerator uniform)
Constructs a new instance with the given parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GaussianProposalFunction
A proposal generator where each perturbation is a random vector, where each element is drawn
from a standard Normal distribution, multiplied by a scale matrix.
|
class |
HybridMCMCProposalFunction |
class |
ProposalFunction
A proposal function goes from the current state to the next state, where a state is a vector.
|
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