Uses of Interface
dev.nm.analysis.function.rn2r1.RealScalarFunction
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Uses of RealScalarFunction in dev.nm.algebra.linear.vector.doubles.operation
Methods in dev.nm.algebra.linear.vector.doubles.operation with parameters of type RealScalarFunction Modifier and Type Method Description static Vector
VectorFactory. foreachColumn(Matrix matrix, RealScalarFunction f)
Constructs a vector in which each entry is the result of applying aRealScalarFunction
to each column of an input matrix.static Vector
VectorFactory. foreachRow(Matrix matrix, RealScalarFunction f)
Constructs a vector in which each entry is the result of applying aRealScalarFunction
to each row of an input matrix.static Vector
VectorFactory. foreachVector(Vector[] vectors, RealScalarFunction f)
Applies aRealScalarFunction
on each input vector.static Vector
VectorFactory. foreachVector(Collection<Vector> vectors, RealScalarFunction f)
Applies aRealScalarFunction
on each input vector. -
Uses of RealScalarFunction in dev.nm.analysis.curvefit.interpolation
Classes in dev.nm.analysis.curvefit.interpolation that implement RealScalarFunction Modifier and Type Class Description class
LinearInterpolator
Define a univariate function by linearly interpolating between adjacent points.class
NevilleTable
Neville's algorithm is a polynomial interpolation algorithm. -
Uses of RealScalarFunction in dev.nm.analysis.curvefit.interpolation.bivariate
Methods in dev.nm.analysis.curvefit.interpolation.bivariate that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
BicubicInterpolation. interpolate(BivariateGrid grid)
RealScalarFunction
BicubicSpline. interpolate(BivariateGrid grid)
RealScalarFunction
BilinearInterpolation. interpolate(BivariateGrid grid)
RealScalarFunction
BivariateGridInterpolation. interpolate(BivariateGrid grid)
Constructs a real valued function from a grid of observations. -
Uses of RealScalarFunction in dev.nm.analysis.curvefit.interpolation.multivariate
Methods in dev.nm.analysis.curvefit.interpolation.multivariate that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
MultivariateGridInterpolation. interpolate(MultivariateGrid grid)
Construct a real valued function from a grid of observations.RealScalarFunction
RecursiveGridInterpolation. interpolate(MultivariateGrid grid)
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Uses of RealScalarFunction in dev.nm.analysis.differentialequation.ode.ivp.problem
Methods in dev.nm.analysis.differentialequation.ode.ivp.problem that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
ODE. F()
Get the differential, \(y^{(n)} = F\).Constructors in dev.nm.analysis.differentialequation.ode.ivp.problem with parameters of type RealScalarFunction Constructor Description ODE(RealScalarFunction F, double[] initials, double x0, double x1)
Construct an ODE of order n together with its initial values.ODE1stOrder(RealScalarFunction[] Y, double[] y0, double x0, double x1)
Constructs a system of first order ODEs {Yi} with their initial values {yi0}. -
Uses of RealScalarFunction in dev.nm.analysis.differentiation
Classes in dev.nm.analysis.differentiation that implement RealScalarFunction Modifier and Type Class Description class
Ridders
Ridders' method computes the numerical derivative of a function.Constructors in dev.nm.analysis.differentiation with parameters of type RealScalarFunction Constructor Description Ridders(RealScalarFunction f, int[] varidx)
Construct the derivative function of a vector-valued function using Ridder's method.Ridders(RealScalarFunction f, int[] varidx, double rate, int discretization)
Construct the derivative function of a vector-valued function using Ridder's method. -
Uses of RealScalarFunction in dev.nm.analysis.differentiation.multivariate
Classes in dev.nm.analysis.differentiation.multivariate that implement RealScalarFunction Modifier and Type Class Description class
MultivariateFiniteDifference
A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.Constructors in dev.nm.analysis.differentiation.multivariate with parameters of type RealScalarFunction Constructor Description BorderedHessian(RealScalarFunction f, RealScalarFunction g, Vector x)
Construct the bordered Hessian matrix for multivariate functions f and g at point x.Gradient(RealScalarFunction f, Vector x)
Construct the gradient vector for a multivariate function f at point x.GradientFunction(RealScalarFunction f)
Construct the gradient function of a real scalar function f.Hessian(RealScalarFunction f, Vector x)
Construct the Hessian matrix for a multivariate function f at point x.HessianFunction(RealScalarFunction f)
Construct the Hessian function of a real scalar function f.Jacobian(RealScalarFunction[] f, Vector x)
Construct the Jacobian matrix for a multivariate function f at point x.MultivariateFiniteDifference(RealScalarFunction f, int[] varidx)
Construct the partial derivative of a multi-variable function.Constructor parameters in dev.nm.analysis.differentiation.multivariate with type arguments of type RealScalarFunction Constructor Description Jacobian(List<RealScalarFunction> f, Vector x)
Construct the Jacobian matrix for a multivariate function f at point x. -
Uses of RealScalarFunction in dev.nm.analysis.differentiation.univariate
Classes in dev.nm.analysis.differentiation.univariate that implement RealScalarFunction Modifier and Type Class Description class
DBeta
This is the first order derivative function of theBeta
function w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\).class
DBetaRegularized
This is the first order derivative function of the Regularized Incomplete Beta function,BetaRegularized
, w.r.t the upper limit, x.class
DErf
This is the first order derivative function of the Error function,Erf
.class
Dfdx
The first derivative is a measure of how a function changes as its input changes.class
DGamma
This is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).class
DGaussian
This is the first order derivative function of aGaussian
function, \({d \mathrm{\phi}(x) \over dx}\).class
DPolynomial
This is the first order derivative function of aPolynomial
, which, again, is a polynomial.class
FiniteDifference
A finite difference (divided by a small increment) is an approximation of the derivative of a function. -
Uses of RealScalarFunction in dev.nm.analysis.function.polynomial
Classes in dev.nm.analysis.function.polynomial that implement RealScalarFunction Modifier and Type Class Description class
CauchyPolynomial
The Cauchy's polynomial of a polynomial takes this form:class
Polynomial
A polynomial is aUnivariateRealFunction
that represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.class
QuadraticMonomial
A quadratic monomial has this form: x2 + ux + v.class
ScaledPolynomial
This constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow. -
Uses of RealScalarFunction in dev.nm.analysis.function.rn2r1
Subinterfaces of RealScalarFunction in dev.nm.analysis.function.rn2r1 Modifier and Type Interface Description interface
BivariateRealFunction
A bivariate real function takes two real arguments and outputs one real value.interface
TrivariateRealFunction
A trivariate real function takes three real arguments and outputs one real value.Classes in dev.nm.analysis.function.rn2r1 that implement RealScalarFunction Modifier and Type Class Description class
AbstractBivariateRealFunction
A bivariate real function takes two real arguments and outputs one real value.class
AbstractRealScalarFunction
This abstract implementation implementsFunction.dimensionOfRange()
by always returning 1, andFunction.dimensionOfDomain()
by returning the input argument for the dimension of domain.class
AbstractTrivariateRealFunction
A trivariate real function takes three real arguments and outputs one real value.class
QuadraticFunction
A quadratic function takes this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).class
R1Projection
Projection creates a real-valued functionRealScalarFunction
from a vector-valued functionRealVectorFunction
by taking only one of its coordinate components in the vector output.class
RealScalarSubFunction
This constructs aRealScalarFunction
from anotherRealScalarFunction
by restricting/fixing the values of a subset of variables.Constructors in dev.nm.analysis.function.rn2r1 with parameters of type RealScalarFunction Constructor Description RealScalarSubFunction(RealScalarFunction f, Map<Integer,Double> fixing)
Construct a scalar sub-function. -
Uses of RealScalarFunction in dev.nm.analysis.function.rn2r1.univariate
Subinterfaces of RealScalarFunction in dev.nm.analysis.function.rn2r1.univariate Modifier and Type Interface Description interface
UnivariateRealFunction
A univariate real function takes one real argument and outputs one real value.Classes in dev.nm.analysis.function.rn2r1.univariate that implement RealScalarFunction Modifier and Type Class Description class
AbstractUnivariateRealFunction
A univariate real function takes one real argument and outputs one real value.class
ContinuedFraction
A continued fraction representation of a number has this form: \[ z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}} \] ai and bi can be functions of x, which in turn makes z a function of x.class
StepFunction
A step function (or staircase function) is a finite linear combination of indicator functions of intervals. -
Uses of RealScalarFunction in dev.nm.analysis.function.special
Classes in dev.nm.analysis.function.special that implement RealScalarFunction Modifier and Type Class Description class
Rastrigin
The Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms. -
Uses of RealScalarFunction in dev.nm.analysis.function.special.beta
Classes in dev.nm.analysis.function.special.beta that implement RealScalarFunction Modifier and Type Class Description class
Beta
The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]class
BetaRegularized
The Regularized Incomplete Beta function is defined as: \[ I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0 \]class
BetaRegularizedInverse
The inverse of the Regularized Incomplete Beta function is defined at: \[ x = I^{-1}_{(p,q)}(u), 0 \le u \le 1 \]class
LogBeta
This class represents the log of Beta functionlog(B(x, y))
.class
MultinomialBetaFunction
A multinomial Beta function is defined as: \[ \frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\left(\sum_{i=1}^K \alpha_i\right)},\qquad\boldsymbol{\alpha}=(\alpha_1,\cdots,\alpha_K) \] -
Uses of RealScalarFunction in dev.nm.analysis.function.special.gamma
Classes in dev.nm.analysis.function.special.gamma that implement RealScalarFunction Modifier and Type Class Description class
Digamma
The digamma function is defined as the logarithmic derivative of the gamma function.class
GammaGergoNemes
The Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.class
GammaLanczos
Lanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.class
GammaLanczosQuick
Lanczos approximation, computations are done indouble
.class
GammaLowerIncomplete
The Lower Incomplete Gamma function is defined as: \[ \gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s) \] P(s,x) is the Regularized Incomplete Gamma P function.class
GammaRegularizedP
The Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]class
GammaRegularizedPInverse
The inverse of the Regularized Incomplete Gamma P function is defined as: \[ x = P^{-1}(s,u), 0 \geq u \geq 1 \] Whens > 1
, we use the asymptotic inversion method. Whens <= 1
, we use an approximation of P(s,x) together with a higher-order Newton like method. In both cases, the estimated value is then improved using Halley's method, c.f.,HalleyRoot
.class
GammaRegularizedQ
The Regularized Incomplete Gamma Q function is defined as: \[ Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0 \] The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.class
GammaUpperIncomplete
The Upper Incomplete Gamma function is defined as: \[ \Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s) \] The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable.class
LogGamma
The log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.class
Trigamma
The trigamma function is defined as the logarithmic derivative of the digamma function. -
Uses of RealScalarFunction in dev.nm.analysis.function.special.gaussian
Classes in dev.nm.analysis.function.special.gaussian that implement RealScalarFunction Modifier and Type Class Description class
CumulativeNormalHastings
Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.class
CumulativeNormalInverse
The inverse of the cumulative standard Normal distribution function is defined as: \[ N^{-1}(u) /]class
CumulativeNormalMarsaglia
Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.class
Erf
The Error function is defined as: \[ \operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt \]class
Erfc
This complementary Error function is defined as: \[ \operatorname{erfc}(x) = 1-\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt \]class
ErfInverse
The inverse of the Error function is defined as: \[ \operatorname{erf}^{-1}(x) \]class
Gaussian
The Gaussian function is defined as: \[ f(x) = a e^{- { \frac{(x-b)^2 }{ 2 c^2} } } \] -
Uses of RealScalarFunction in dev.nm.analysis.root.multivariate
Methods in dev.nm.analysis.root.multivariate with parameters of type RealScalarFunction Modifier and Type Method Description Vector
NewtonSystemRoot. solve(RealScalarFunction[] f, Vector guess)
Searches for a root, x such that f(x) = 0. -
Uses of RealScalarFunction in dev.nm.misc.algorithm
Methods in dev.nm.misc.algorithm with parameters of type RealScalarFunction Modifier and Type Method Description double[]
Bins. getBinKeyValues(RealScalarFunction f)
Applies a function to the key of each bin. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.constraint
Methods in dev.nm.solver.multivariate.constrained.constraint that return types with arguments of type RealScalarFunction Modifier and Type Method Description List<RealScalarFunction>
Constraints. getConstraints()
Get the list of constraint functions. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.constraint.general
Methods in dev.nm.solver.multivariate.constrained.constraint.general that return types with arguments of type RealScalarFunction Modifier and Type Method Description List<RealScalarFunction>
GeneralConstraints. getConstraints()
Get the constraints.Constructors in dev.nm.solver.multivariate.constrained.constraint.general with parameters of type RealScalarFunction Constructor Description GeneralConstraints(RealScalarFunction... constraints)
Construct an instance of constraints from an array of real-valued functions.GeneralEqualityConstraints(RealScalarFunction... constraints)
Constructs an instance of equality constraints from an array of real-valued functions.GeneralGreaterThanConstraints(RealScalarFunction... constraints)
Construct an instance of greater-than-or-equal-to inequality constraints from an array of real-valued functions.GeneralLessThanConstraints(RealScalarFunction... constraints)
Construct an instance of less-than or equal-to inequality constraints from an array of real-valued functions.Constructor parameters in dev.nm.solver.multivariate.constrained.constraint.general with type arguments of type RealScalarFunction Constructor Description GeneralConstraints(Collection<RealScalarFunction> constraints)
Construct an instance of constraints from a collection of real-valued functions.GeneralEqualityConstraints(Collection<RealScalarFunction> constraints)
Constructs an instance of equality constraints from a collection of real-valued functions.GeneralGreaterThanConstraints(Collection<RealScalarFunction> constraints)
Construct an instance of greater-than-or-equal-to inequality constraints from a collection of real-valued functions.GeneralLessThanConstraints(Collection<RealScalarFunction> constraints)
Construct an instance of less-than or equal-to inequality constraints from a collection of real-valued functions. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.constraint.linear
Methods in dev.nm.solver.multivariate.constrained.constraint.linear that return types with arguments of type RealScalarFunction Modifier and Type Method Description List<RealScalarFunction>
LinearConstraints. getConstraints()
Constructors in dev.nm.solver.multivariate.constrained.constraint.linear with parameters of type RealScalarFunction Constructor Description LowerBoundConstraints(RealScalarFunction f, double lower)
Construct a lower bound constraints for all variables in a function.NonNegativityConstraints(RealScalarFunction f)
Construct a lower bound constraints for all variables in a function.UpperBoundConstraints(RealScalarFunction f, double lower)
Construct an upper bound constraints for all variables in a function. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.convex.sdp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.problem that return types with arguments of type RealScalarFunction Modifier and Type Method Description List<RealScalarFunction>
SDPDualProblem.EqualityConstraints. getConstraints()
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
SOCPDualProblem. f()
RealScalarFunction
SOCPDualProblem1. f()
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem that return types with arguments of type RealScalarFunction Modifier and Type Method Description List<RealScalarFunction>
SOCPDualProblem.EqualityConstraints. getConstraints()
List<RealScalarFunction>
SOCPDualProblem1.EqualityConstraints. getConstraints()
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Classes in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization that implement RealScalarFunction Modifier and Type Class Description class
MarketImpact1
Constructs the constraint coefficient arrays of a market impact term in the compact form.class
PortfolioRiskExactSigma
Constructs the constraint coefficient arrays of the portfolio risk term in the compact form.class
SOCPPortfolioConstraint
An SOCP constraint for portfolio optimization, e.g., market impact, is represented by a set of constraints in this form: \[ ||A^{T}x+c||_{2}\leq b^{T}x+d \] or this form: /[ A^T x = c, x \in \Re^m /] or this form: /[ A^T x \leq c, x \in \Re^m /]class
SOCPPortfolioObjectiveFunction
Constructs the objective function for portfolio optimization.class
SOCPRiskConstraint
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
LPProblemImpl1. f()
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
LPRevisedSimplexSolver.Problem. f()
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
Classes in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem that implement RealScalarFunction Modifier and Type Class Description class
QPProblemOnlyEqualityConstraints
A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.general.penaltymethod
Classes in dev.nm.solver.multivariate.constrained.general.penaltymethod that implement RealScalarFunction Modifier and Type Class Description class
AbsoluteErrorPenalty
This penalty function sums up the absolute error penalties.class
CourantPenalty
This penalty function sums up the squared error penalties.class
FletcherPenalty
This penalty function sums up the squared costs penalties.class
MultiplierPenalty
A multiplier penalty function allows different weights to be assigned to the constraints.class
PenaltyFunction
A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.class
SumOfPenalties
This penalty function sums up the costs from a set of constituent penalty functions.class
ZeroPenalty
This is a dummy zero cost (no cost) penalty function. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.general.sqp.activeset
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset with parameters of type RealScalarFunction Modifier and Type Method Description SQPASVariation
SQPActiveSetMinimizer.VariationFactory. newVariation(RealScalarFunction f, RealVectorFunction g, EqualityConstraints equal, GreaterThanConstraints greater)
Construct a new instance ofSQPASVariation
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.IterativeSolution<Vector>
SQPActiveSetOnlyInequalityConstraintMinimizer. solve(RealScalarFunction f, GreaterThanConstraints greater)
Minimize a function subject to only inequality constraints.Constructors in dev.nm.solver.multivariate.constrained.general.sqp.activeset with parameters of type RealScalarFunction Constructor Description Solution(RealScalarFunction f, RealVectorFunction g, EqualityConstraints equal, GreaterThanConstraints greater)
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
Fields in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint declared as RealScalarFunction Modifier and Type Field Description protected RealScalarFunction
SQPASEVariation1. f
Fields in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint with type parameters of type RealScalarFunction Modifier and Type Field Description protected List<RealScalarFunction>
SQPASEVariation1. a
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint with parameters of type RealScalarFunction Modifier and Type Method Description SQPASEVariation
SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory. newVariation(RealScalarFunction f, EqualityConstraints equal)
Construct a new instance ofSQPASEVariation
for an SQP problem.void
SQPASEVariation1. set(RealScalarFunction f, EqualityConstraints equal)
Associate this variation to a particular general constrained minimization problem with only equality constraints.IterativeSolution<Vector>
SQPActiveSetOnlyEqualityConstraint1Minimizer. solve(RealScalarFunction f, EqualityConstraints equal)
Minimize a function subject to only equality constraints. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.integer
Methods in dev.nm.solver.multivariate.constrained.integer that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
IPProblemImpl1. f()
Constructors in dev.nm.solver.multivariate.constrained.integer with parameters of type RealScalarFunction Constructor Description IPProblemImpl1(RealScalarFunction f, EqualityConstraints equal, LessThanConstraints less, int[] integers)
Construct a constrained optimization problem with integral constraints.IPProblemImpl1(RealScalarFunction f, EqualityConstraints equal, LessThanConstraints less, int[] integers, double epsilon)
Construct a constrained optimization problem with integral constraints. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.integer.bruteforce
Constructors in dev.nm.solver.multivariate.constrained.integer.bruteforce with parameters of type RealScalarFunction Constructor Description BruteForceIPProblem(RealScalarFunction f, EqualityConstraints equal, LessThanConstraints less, BruteForceIPProblem.IntegerDomain[] integers, double epsilon)
Construct an integral constrained minimization problem with explicit integral domains.BruteForceIPProblem(RealScalarFunction f, BruteForceIPProblem.IntegerDomain[] integers, double epsilon)
Construct an integral constrained minimization problem with explicit integral domains. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.integer.linear.bb
Methods in dev.nm.solver.multivariate.constrained.integer.linear.bb that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
ILPNode. f()
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.integer.linear.problem
Methods in dev.nm.solver.multivariate.constrained.integer.linear.problem that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
ILPProblemImpl1. f()
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Uses of RealScalarFunction in dev.nm.solver.multivariate.constrained.problem
Methods in dev.nm.solver.multivariate.constrained.problem that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
BoxOptimProblem. f()
RealScalarFunction
ConstrainedOptimProblemImpl1. f()
RealScalarFunction
NonNegativityConstraintOptimProblem. f()
Constructors in dev.nm.solver.multivariate.constrained.problem with parameters of type RealScalarFunction Constructor Description BoxOptimProblem(RealScalarFunction f, Vector lower, Vector upper)
Constructs an optimization problem with box constraints.BoxOptimProblem(RealScalarFunction f, BoxConstraints box)
Constructs an optimization problem with box constraints.ConstrainedOptimProblemImpl1(RealScalarFunction f, EqualityConstraints equal, LessThanConstraints less)
Constructs a constrained optimization problem.NonNegativityConstraintOptimProblem(RealScalarFunction f)
Construct a constrained optimization problem with only non-negative variables. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim with parameters of type RealScalarFunction Modifier and Type Method Description Best2Bin.DeBest2BinCell
Best2Bin. getSimpleCell(RealScalarFunction f, Vector x)
Rand1Bin.DeRand1BinCell
Rand1Bin. getSimpleCell(RealScalarFunction f, Vector x)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim with parameters of type RealScalarFunction Constructor Description DeBest2BinCell(RealScalarFunction f, Vector x)
DeOptimCell(RealScalarFunction f, Vector x)
DeRand1BinCell(RealScalarFunction f, Vector x)
Solution(RealScalarFunction f)
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Uses of RealScalarFunction in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained with parameters of type RealScalarFunction Modifier and Type Method Description abstract ConstrainedCellFactory.ConstrainedCell
ConstrainedCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Override this method to put in whatever constraints in the minimization problem.ConstrainedCellFactory.ConstrainedCell
IntegralConstrainedCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained with parameters of type RealScalarFunction Constructor Description ConstrainedCell(RealScalarFunction f, Vector x)
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Uses of RealScalarFunction in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local with parameters of type RealScalarFunction Modifier and Type Method Description LocalSearchCellFactory.LocalSearchCell
LocalSearchCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local with parameters of type RealScalarFunction Constructor Description LocalSearchCell(RealScalarFunction f, Vector x)
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Uses of RealScalarFunction in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
Fields in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid declared as RealScalarFunction Modifier and Type Field Description protected RealScalarFunction
SimpleGridMinimizer.Solution. f
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
RealScalarFunctionChromosome. f()
Get the objective function.Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid with parameters of type RealScalarFunction Modifier and Type Method Description SimpleCellFactory.SimpleCell
SimpleCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Construct an instance of aSimpleCell
.Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid with parameters of type RealScalarFunction Constructor Description RealScalarFunctionChromosome(RealScalarFunction f, Vector x)
Construct an instance ofRealScalarFunctionChromosome
.SimpleCell(RealScalarFunction f, Vector x)
Solution(RealScalarFunction f)
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Uses of RealScalarFunction in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration with parameters of type RealScalarFunction Constructor Description PerturbationAroundPoint(RealScalarFunction f, SimpleCellFactory factory, int poolSize, Vector var, Vector initial0, long seed)
Generate an initial pool of chromosomes by adding a variance around a given initial.UniformMeshOverRegion(RealScalarFunction f, SimpleCellFactory factory, RandomLongGenerator uniform, int minDiscretization, Vector[] initials0, double epsilon)
Generate an initial pool of chromosomes by putting a uniform mesh/grid/net over the entire region. -
Uses of RealScalarFunction in dev.nm.solver.multivariate.minmax
Methods in dev.nm.solver.multivariate.minmax that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
MinMaxProblem. error(T omega)
e(x, ω) is the error function, or the minmax objective, for a given ω. -
Uses of RealScalarFunction in dev.nm.solver.problem
Methods in dev.nm.solver.problem that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
C2OptimProblemImpl. f()
RealScalarFunction
OptimProblem. f()
Get the objective function.Constructors in dev.nm.solver.problem with parameters of type RealScalarFunction Constructor Description C2OptimProblemImpl(RealScalarFunction f)
Construct an optimization problem with an objective function.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. -
Uses of RealScalarFunction in dev.nm.stat.distribution.multivariate.exponentialfamily
Constructors in dev.nm.stat.distribution.multivariate.exponentialfamily with parameters of type RealScalarFunction Constructor Description MultivariateExponentialFamily(RealScalarFunction h, RealVectorFunction eta, RealVectorFunction T, RealScalarFunction A)
Construct a factory to construct probability distribution in the exponential family of this form. -
Uses of RealScalarFunction in dev.nm.stat.distribution.univariate.exponentialfamily
Constructors in dev.nm.stat.distribution.univariate.exponentialfamily with parameters of type RealScalarFunction Constructor Description ExponentialFamily(UnivariateRealFunction h, RealVectorFunction eta, AbstractR1RnFunction T, RealScalarFunction A)
Construct a factory to construct probability distribution in the exponential family of this form. -
Uses of RealScalarFunction in dev.nm.stat.evt.evd.univariate.fitting
Methods in dev.nm.stat.evt.evd.univariate.fitting that return RealScalarFunction Modifier and Type Method Description RealScalarFunction
EstimateByLogLikelihood. getLogLikelihoodFunction()
Get the log-likelihood function.Constructors in dev.nm.stat.evt.evd.univariate.fitting with parameters of type RealScalarFunction Constructor Description EstimateByLogLikelihood(Vector fittedParameters, RealScalarFunction logLikelihoodFunction)
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Uses of RealScalarFunction in dev.nm.stat.evt.evd.univariate.fitting.acer
Classes in dev.nm.stat.evt.evd.univariate.fitting.acer that implement RealScalarFunction Modifier and Type Class Description class
ACERFunction
The ACER (Average Conditional Exceedance Rate) function \(\epsilon_k(\eta)\) approximates the probability \[ \epsilon_k(\eta) = Pr(X_k > \eta | X_1 \le \eta, X_2 \le \eta, ..., X_{k-1} \le \eta) \] for a sequence of stochastic process observations \(X_i\) with a k-step memory.class
ACERInverseFunction
The inverse of the ACER function.class
ACERLogFunction
The ACER function in log scale (base e), i.e., \(log(\epsilon_k(\eta))\).class
ACERReturnLevel
Given an ACER function, compute the return level \(\eta\) for a given return period \(R\). -
Uses of RealScalarFunction in dev.nm.stat.evt.function
Classes in dev.nm.stat.evt.function that implement RealScalarFunction Modifier and Type Class Description class
ReturnLevel
Given a GEV distribution of a random variable \(X\), the return level \(\eta\) is the value that is expected to be exceeded on average once every interval of time \(T\), with a probability of \(1 / T\).class
ReturnPeriod
The return period \(R\) of a level \(\eta\) for a random variable \(X\) is the mean number of trials that must be done for \(X\) to exceed \(\eta\). -
Uses of RealScalarFunction in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
Methods in dev.nm.stat.random.rng.multivariate.mcmc.hybrid with parameters of type RealScalarFunction Modifier and Type Method Description static double
AbstractHybridMCMC. H(LeapFrogging.DynamicsState state, RealScalarFunction logF, Vector m)
Evaluates a system's total energy at a given state.Constructors in dev.nm.stat.random.rng.multivariate.mcmc.hybrid with parameters of type RealScalarFunction Constructor Description HybridMCMC(RealScalarFunction logF, RealVectorFunction dLogF, Vector m, double dt, int L, Vector initialState, RandomLongGenerator rlg)
Constructs a new instance with the given parameters.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.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. -
Uses of RealScalarFunction in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Methods in dev.nm.stat.random.rng.multivariate.mcmc.metropolis with parameters of type RealScalarFunction Modifier and Type Method Description static boolean
MetropolisUtils. isProposalAccepted(RealScalarFunction logf, RandomLongGenerator uniform, Vector currentState, Vector proposedState)
Uses the given LOG density function to determine whether the given state transition should be accepted.static double
MetropolisUtils. logAcceptanceRatio(RealScalarFunction logf, Vector currentState, Vector proposedState)
Computes the log of the acceptance ratio.Constructors in dev.nm.stat.random.rng.multivariate.mcmc.metropolis with parameters of type RealScalarFunction Constructor Description Metropolis(RealScalarFunction logf, Vector initialState, double sigma, RandomLongGenerator uniform)
Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, with the given variance and zero covariance.Metropolis(RealScalarFunction logf, Vector initialState, Matrix scale, RandomLongGenerator uniform)
Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, multiplied by the given scale matrix.Metropolis(RealScalarFunction logf, RealVectorFunction proposalFunction, Vector initialState, RandomLongGenerator uniform)
Constructs a new instance with the given parameters.MetropolisHastings(RealScalarFunction logf, ProposalFunction proposalFunction, MetropolisHastings.ProposalDensityFunction proposalDensity, Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters.RobustAdaptiveMetropolis(RealScalarFunction logf, double targetAcceptance, Vector initialState, RandomLongGenerator uniform)
Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of 0.5.RobustAdaptiveMetropolis(RealScalarFunction logf, Matrix initialScale, double gamma, double targetAcceptance, Vector initialState, RandomStandardNormalGenerator rnorm, RandomLongGenerator uniform)
Constructs a new instance with the given parameters. -
Uses of RealScalarFunction in dev.nm.stat.regression.linear.logistic
Methods in dev.nm.stat.regression.linear.logistic that return RealScalarFunction Modifier and Type Method Description static RealScalarFunction
LogisticRegression. logLikelihood(LogisticProblem problem)
Constructs the log-likelihood function for a logistic regression problem. -
Uses of RealScalarFunction in dev.nm.stat.stochasticprocess.univariate.filtration
Classes in dev.nm.stat.stochasticprocess.univariate.filtration that implement RealScalarFunction Modifier and Type Class Description class
Bt
This is aFiltrationFunction
that returns \(B(t_i)\), the Brownian motion value at the i-th time point.class
F_Sum_BtDt
This represents a function of this integral \[ I = \int_{0}^{1} B(t)dt \]class
F_Sum_tBtDt
This represents a function of this integral \[ \int_{0}^{1} (t - 0.5) * B(t) dt \]class
FiltrationFunction
A filtration function, parameterized by a fixed filtration, is a function of time, \(f(\mathfrak{F_{t_i}})\). -
Uses of RealScalarFunction in dev.nm.stat.timeseries.linear.univariate
Classes in dev.nm.stat.timeseries.linear.univariate that implement RealScalarFunction Modifier and Type Class Description class
AutoCorrelationFunction
This is the auto-correlation function of a univariate time series {xt}.class
AutoCovarianceFunction
This is the auto-covariance function of a univariate time series {xt}. -
Uses of RealScalarFunction in dev.nm.stat.timeseries.linear.univariate.sample
Classes in dev.nm.stat.timeseries.linear.univariate.sample that implement RealScalarFunction Modifier and Type Class Description class
SampleAutoCorrelation
This is the sample Auto-Correlation Function (ACF) for a univariate data set.class
SampleAutoCovariance
This is the sample Auto-Covariance Function (ACVF) for a univariate data set.class
SamplePartialAutoCorrelation
This is the sample partial Auto-Correlation Function (PACF) for a univariate data set. -
Uses of RealScalarFunction in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that implement RealScalarFunction Modifier and Type Class Description class
AutoCorrelation
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.class
AutoCovariance
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion. -
Uses of RealScalarFunction in tech.nmfin.portfoliooptimization.lai2010.ceta
Classes in tech.nmfin.portfoliooptimization.lai2010.ceta that implement RealScalarFunction Modifier and Type Class Description class
Ceta
The function C(η) to be maximized (Eq. -
Uses of RealScalarFunction in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
Classes in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer that implement RealScalarFunction Modifier and Type Class Description static class
CetaMaximizer.NegCetaFunction
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Uses of RealScalarFunction in tech.nmfin.portfoliooptimization.socp.constraints
Classes in tech.nmfin.portfoliooptimization.socp.constraints that implement RealScalarFunction Modifier and Type Class Description class
SOCPLinearBlackList
A black list means that the positions of some assets must be zero.class
SOCPLinearMaximumLoan
A maximum loan constraint.class
SOCPLinearSectorNeutrality
A sector neutrality means that the sum of weights for given sectors are zero.class
SOCPLinearSelfFinancing
A self financing constraint.class
SOCPLinearZeroValue
A zero value constraint.class
SOCPMaximumLoan
Transforms a maximum loan constraint into the compact SOCP form.class
SOCPNoTradingList1
Transforms a black list (not to trade a new position) constraint into the compact SOCP form.class
SOCPSectorNeutrality
Transforms a sector neutral constraint into the compact SOCP form.class
SOCPSelfFinancing
Transforms a self financing constraint into the compact SOCP form.class
SOCPZeroValue
Transforms a zero value constraint into the compact SOCP form. -
Uses of RealScalarFunction in tech.nmfin.portfoliooptimization.socp.constraints.ybar
Classes in tech.nmfin.portfoliooptimization.socp.constraints.ybar that implement RealScalarFunction Modifier and Type Class Description class
SOCPLinearSectorExposure
A sector exposure constraint.class
SOCPNoTradingList2
Transforms a black list (not to trade a new position) constraint into the compact SOCP form.class
SOCPSectorExposure
Transforms a sector exposure constraint into the compact SOCP form.
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