Uses of Interface
dev.nm.analysis.function.Function
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Uses of Function in dev.nm.analysis.curvefit.interpolation
Classes in dev.nm.analysis.curvefit.interpolation that implement Function Modifier and Type Class Description classLinearInterpolatorDefine a univariate function by linearly interpolating between adjacent points.classNevilleTableNeville's algorithm is a polynomial interpolation algorithm. -
Uses of Function in dev.nm.analysis.differentiation
Classes in dev.nm.analysis.differentiation that implement Function Modifier and Type Class Description classRiddersRidders' method computes the numerical derivative of a function. -
Uses of Function in dev.nm.analysis.differentiation.multivariate
Classes in dev.nm.analysis.differentiation.multivariate that implement Function Modifier and Type Class Description classGradientFunctionThe gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.classHessianFunctionThe Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.classJacobianFunctionThe Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x.classMultivariateFiniteDifferenceA partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant. -
Uses of Function in dev.nm.analysis.differentiation.univariate
Classes in dev.nm.analysis.differentiation.univariate that implement Function Modifier and Type Class Description classDBetaThis is the first order derivative function of theBetafunction w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\).classDBetaRegularizedThis is the first order derivative function of the Regularized Incomplete Beta function,BetaRegularized, w.r.t the upper limit, x.classDErfThis is the first order derivative function of the Error function,Erf.classDfdxThe first derivative is a measure of how a function changes as its input changes.classDGammaThis is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).classDGaussianThis is the first order derivative function of aGaussianfunction, \({d \mathrm{\phi}(x) \over dx}\).classDPolynomialThis is the first order derivative function of aPolynomial, which, again, is a polynomial.classFiniteDifferenceA finite difference (divided by a small increment) is an approximation of the derivative of a function. -
Uses of Function in dev.nm.analysis.function
Classes in dev.nm.analysis.function that implement Function Modifier and Type Class Description classSubFunction<R>A sub-function, g, is defined over a subset of the domain of another (original) function, f.Fields in dev.nm.analysis.function declared as Function Modifier and Type Field Description protected Function<Vector,R>SubFunction. fthe original, unrestricted functionConstructors in dev.nm.analysis.function with parameters of type Function Constructor Description SubFunction(Function<Vector,R> f, Map<Integer,Double> fixing)Constructs a sub-function. -
Uses of Function in dev.nm.analysis.function.matrix
Subinterfaces of Function in dev.nm.analysis.function.matrix Modifier and Type Interface Description interfaceRntoMatrixThis interface is a function that maps from Rn to a Matrix space.Classes in dev.nm.analysis.function.matrix that implement Function Modifier and Type Class Description classR1toConstantMatrixA constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).classR1toMatrixThis is a function that maps from R1 to a Matrix space.classR2toMatrixThis is a function that maps from R2 to a Matrix space. -
Uses of Function in dev.nm.analysis.function.polynomial
Classes in dev.nm.analysis.function.polynomial that implement Function Modifier and Type Class Description classCauchyPolynomialThe Cauchy's polynomial of a polynomial takes this form:classPolynomialA polynomial is aUnivariateRealFunctionthat represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.classQuadraticMonomialA quadratic monomial has this form: x2 + ux + v.classScaledPolynomialThis constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow. -
Uses of Function in dev.nm.analysis.function.rn2r1
Subinterfaces of Function in dev.nm.analysis.function.rn2r1 Modifier and Type Interface Description interfaceBivariateRealFunctionA bivariate real function takes two real arguments and outputs one real value.interfaceRealScalarFunctionA real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).interfaceTrivariateRealFunctionA trivariate real function takes three real arguments and outputs one real value.Classes in dev.nm.analysis.function.rn2r1 that implement Function Modifier and Type Class Description classAbstractBivariateRealFunctionA bivariate real function takes two real arguments and outputs one real value.classAbstractRealScalarFunctionThis abstract implementation implementsdimensionOfRange()by always returning 1, anddimensionOfDomain()by returning the input argument for the dimension of domain.classAbstractTrivariateRealFunctionA trivariate real function takes three real arguments and outputs one real value.classQuadraticFunctionA quadratic function takes this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).classR1ProjectionProjection creates a real-valued functionRealScalarFunctionfrom a vector-valued functionRealVectorFunctionby taking only one of its coordinate components in the vector output.classRealScalarSubFunctionThis constructs aRealScalarFunctionfrom anotherRealScalarFunctionby restricting/fixing the values of a subset of variables. -
Uses of Function in dev.nm.analysis.function.rn2r1.univariate
Subinterfaces of Function in dev.nm.analysis.function.rn2r1.univariate Modifier and Type Interface Description interfaceUnivariateRealFunctionA univariate real function takes one real argument and outputs one real value.Classes in dev.nm.analysis.function.rn2r1.univariate that implement Function Modifier and Type Class Description classAbstractUnivariateRealFunctionA univariate real function takes one real argument and outputs one real value.classContinuedFractionA 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.classStepFunctionA step function (or staircase function) is a finite linear combination of indicator functions of intervals. -
Uses of Function in dev.nm.analysis.function.rn2rm
Subinterfaces of Function in dev.nm.analysis.function.rn2rm Modifier and Type Interface Description interfaceRealVectorFunctionA vector-valued function a \(R^n \rightarrow R^m\) function, \([y_1,...,y_m] = f(x_1,...,x_n)\).Classes in dev.nm.analysis.function.rn2rm that implement Function Modifier and Type Class Description classAbstractR1RnFunctionThis is a function that takes one real argument and outputs one vector value.classAbstractRealVectorFunctionThis abstract implementation implementsdimensionOfDomain()anddimensionOfRange()by returning the input arguments at constructor.classRealVectorSubFunctionThis constructs aRealVectorFunctionfrom anotherRealVectorFunctionby restricting/fixing the values of a subset of variables. -
Uses of Function in dev.nm.analysis.function.special
Classes in dev.nm.analysis.function.special that implement Function Modifier and Type Class Description classRastriginThe Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms. -
Uses of Function in dev.nm.analysis.function.special.beta
Classes in dev.nm.analysis.function.special.beta that implement Function Modifier and Type Class Description classBetaThe beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]classBetaRegularizedThe Regularized Incomplete Beta function is defined as: \[ I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0 \]classBetaRegularizedInverseThe inverse of the Regularized Incomplete Beta function is defined at: \[ x = I^{-1}_{(p,q)}(u), 0 \le u \le 1 \]classLogBetaThis class represents the log of Beta functionlog(B(x, y)).classMultinomialBetaFunctionA 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 Function in dev.nm.analysis.function.special.gamma
Classes in dev.nm.analysis.function.special.gamma that implement Function Modifier and Type Class Description classDigammaThe digamma function is defined as the logarithmic derivative of the gamma function.classGammaGergoNemesThe Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.classGammaLanczosLanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.classGammaLanczosQuickLanczos approximation, computations are done indouble.classGammaLowerIncompleteThe 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.classGammaRegularizedPThe Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]classGammaRegularizedPInverseThe 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.classGammaRegularizedQThe 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.classGammaUpperIncompleteThe 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.classLogGammaThe log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.classTrigammaThe trigamma function is defined as the logarithmic derivative of the digamma function. -
Uses of Function in dev.nm.analysis.function.special.gaussian
Classes in dev.nm.analysis.function.special.gaussian that implement Function Modifier and Type Class Description classCumulativeNormalHastingsHastings algorithm is faster but less accurate way to compute the cumulative standard Normal.classCumulativeNormalInverseThe inverse of the cumulative standard Normal distribution function is defined as: \[ N^{-1}(u) /]classCumulativeNormalMarsagliaMarsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.classErfThe Error function is defined as: \[ \operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt \]classErfcThis complementary Error function is defined as: \[ \operatorname{erfc}(x) = 1-\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt \]classErfInverseThe inverse of the Error function is defined as: \[ \operatorname{erf}^{-1}(x) \]classGaussianThe Gaussian function is defined as: \[ f(x) = a e^{- { \frac{(x-b)^2 }{ 2 c^2} } } \] -
Uses of Function in dev.nm.misc.algorithm
Methods in dev.nm.misc.algorithm with parameters of type Function Modifier and Type Method Description double[]Bins. getBinObjectValues(Function<List<T>,Double> f)Applies a function to the items of each bin.Constructors in dev.nm.misc.algorithm with parameters of type Function Constructor Description BruteForce(Function<D,R> function)Constructs a brute force search for a function. -
Uses of Function 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 Function Modifier and Type Class Description classMarketImpact1Constructs the constraint coefficient arrays of a market impact term in the compact form.classPortfolioRiskExactSigmaConstructs the constraint coefficient arrays of the portfolio risk term in the compact form.classSOCPPortfolioConstraintAn 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 /]classSOCPPortfolioObjectiveFunctionConstructs the objective function for portfolio optimization.classSOCPRiskConstraint -
Uses of Function 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 Function Modifier and Type Class Description classQPProblemOnlyEqualityConstraintsA 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 Function in dev.nm.solver.multivariate.constrained.general.penaltymethod
Classes in dev.nm.solver.multivariate.constrained.general.penaltymethod that implement Function Modifier and Type Class Description classAbsoluteErrorPenaltyThis penalty function sums up the absolute error penalties.classCourantPenaltyThis penalty function sums up the squared error penalties.classFletcherPenaltyThis penalty function sums up the squared costs penalties.classMultiplierPenaltyA multiplier penalty function allows different weights to be assigned to the constraints.classPenaltyFunctionA function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.classSumOfPenaltiesThis penalty function sums up the costs from a set of constituent penalty functions.classZeroPenaltyThis is a dummy zero cost (no cost) penalty function. -
Uses of Function in dev.nm.solver.multivariate.unconstrained
Methods in dev.nm.solver.multivariate.unconstrained with parameters of type Function Modifier and Type Method Description BruteForceMinimizer.SolutionBruteForceMinimizer. solve(Function<Vector,R> f) -
Uses of Function in dev.nm.stat.evt.evd.univariate.fitting.acer
Classes in dev.nm.stat.evt.evd.univariate.fitting.acer that implement Function Modifier and Type Class Description classACERFunctionThe 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.classACERInverseFunctionThe inverse of the ACER function.classACERLogFunctionThe ACER function in log scale (base e), i.e., \(log(\epsilon_k(\eta))\).classACERReturnLevelGiven an ACER function, compute the return level \(\eta\) for a given return period \(R\). -
Uses of Function in dev.nm.stat.evt.function
Classes in dev.nm.stat.evt.function that implement Function Modifier and Type Class Description classReturnLevelGiven 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 \(1 / T\).classReturnPeriodThe 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 Function in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
Classes in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction that implement Function Modifier and Type Class Description classGaussianProposalFunctionA proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix.classHybridMCMCProposalFunctionclassProposalFunctionA proposal function goes from the current state to the next state, where a state is a vector. -
Uses of Function in dev.nm.stat.stochasticprocess.univariate.filtration
Classes in dev.nm.stat.stochasticprocess.univariate.filtration that implement Function Modifier and Type Class Description classBtThis is aFiltrationFunctionthat returns \(B(t_i)\), the Brownian motion value at the i-th time point.classF_Sum_BtDtThis represents a function of this integral \[ I = \int_{0}^{1} B(t)dt \]classF_Sum_tBtDtThis represents a function of this integral \[ \int_{0}^{1} (t - 0.5) * B(t) dt \]classFiltrationFunctionA filtration function, parameterized by a fixed filtration, is a function of time, \(f(\mathfrak{F_{t_i}})\). -
Uses of Function in dev.nm.stat.timeseries.linear.multivariate
Classes in dev.nm.stat.timeseries.linear.multivariate that implement Function Modifier and Type Class Description classMultivariateAutoCorrelationFunctionThis is the auto-correlation function of a multi-dimensional time series {Xt}.classMultivariateAutoCovarianceFunctionThis is the auto-covariance function of a multi-dimensional time series {Xt}, \[ K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)') \] For a stationary process, the auto-covariance depends only on the lag, |i - j|. -
Uses of Function in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
Classes in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma that implement Function Modifier and Type Class Description classVARMAAutoCorrelationCompute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.classVARMAAutoCovarianceCompute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0. -
Uses of Function in dev.nm.stat.timeseries.linear.univariate
Classes in dev.nm.stat.timeseries.linear.univariate that implement Function Modifier and Type Class Description classAutoCorrelationFunctionThis is the auto-correlation function of a univariate time series {xt}.classAutoCovarianceFunctionThis is the auto-covariance function of a univariate time series {xt}. -
Uses of Function in dev.nm.stat.timeseries.linear.univariate.sample
Classes in dev.nm.stat.timeseries.linear.univariate.sample that implement Function Modifier and Type Class Description classSampleAutoCorrelationThis is the sample Auto-Correlation Function (ACF) for a univariate data set.classSampleAutoCovarianceThis is the sample Auto-Covariance Function (ACVF) for a univariate data set.classSamplePartialAutoCorrelationThis is the sample partial Auto-Correlation Function (PACF) for a univariate data set. -
Uses of Function in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that implement Function Modifier and Type Class Description classAutoCorrelationCompute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.classAutoCovarianceComputes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion. -
Uses of Function in tech.nmfin.portfoliooptimization.lai2010.ceta
Classes in tech.nmfin.portfoliooptimization.lai2010.ceta that implement Function Modifier and Type Class Description classCetaThe function C(η) to be maximized (Eq. -
Uses of Function in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
Classes in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer that implement Function Modifier and Type Class Description static classCetaMaximizer.NegCetaFunction -
Uses of Function in tech.nmfin.portfoliooptimization.socp.constraints
Classes in tech.nmfin.portfoliooptimization.socp.constraints that implement Function Modifier and Type Class Description classSOCPLinearBlackListA black list means that the positions of some assets must be zero.classSOCPLinearMaximumLoanA maximum loan constraint.classSOCPLinearSectorNeutralityA sector neutrality means that the sum of weights for given sectors are zero.classSOCPLinearSelfFinancingA self financing constraint.classSOCPLinearZeroValueA zero value constraint.classSOCPMaximumLoanTransforms a maximum loan constraint into the compact SOCP form.classSOCPNoTradingList1Transforms a black list (not to trade a new position) constraint into the compact SOCP form.classSOCPSectorNeutralityTransforms a sector neutral constraint into the compact SOCP form.classSOCPSelfFinancingTransforms a self financing constraint into the compact SOCP form.classSOCPZeroValueTransforms a zero value constraint into the compact SOCP form. -
Uses of Function in tech.nmfin.portfoliooptimization.socp.constraints.ybar
Classes in tech.nmfin.portfoliooptimization.socp.constraints.ybar that implement Function Modifier and Type Class Description classSOCPLinearSectorExposureA sector exposure constraint.classSOCPNoTradingList2Transforms a black list (not to trade a new position) constraint into the compact SOCP form.classSOCPSectorExposureTransforms a sector exposure constraint into the compact SOCP form.
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