Class LinearFunction<N extends Comparable<N>>

java.lang.Object

org.ojalgo.function.multiary.LinearFunction<N>

All Implemented Interfaces:

BasicFunction, BasicFunction.PlainUnary<Access1D<N>, N>, MultiaryFunction<N>, MultiaryFunction.Linear<N>, MultiaryFunction.TwiceDifferentiable<N>

public final class LinearFunction<N extends Comparable<N>>
extends Object
implements MultiaryFunction.TwiceDifferentiable<N>, MultiaryFunction.Linear<N>

[l]^T[x]

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final class	LinearFunction.Factory<N extends Comparable<N>>

Nested classes/interfaces inherited from interface BasicFunction

BasicFunction.Differentiable<N,F>, BasicFunction.Integratable<N,F>, BasicFunction.PlainUnary<T,R>

Nested classes/interfaces inherited from interface MultiaryFunction

MultiaryFunction.Affine<N>, MultiaryFunction.Constant<N>, MultiaryFunction.Convex<N>, MultiaryFunction.Linear<N>, MultiaryFunction.PureQuadratic<N>, MultiaryFunction.Quadratic<N>, MultiaryFunction.TwiceDifferentiable<N>

Field Summary

Fields

Modifier and Type	Field	Description
private final MatrixStore<N>	myCoefficients

Constructor Summary

Constructors

Constructor	Description
LinearFunction(MatrixStore<N> coefficients)

Method Summary

Modifier and Type	Method	Description
int	arity()
(package private) PhysicalStore.Factory<N,?>	factory()
static <N extends Comparable<N>> LinearFunction.Factory<N>	factory(PhysicalStore.Factory<N,?> factory)
MatrixStore<N>	getGradient(Access1D<N> point)	The gradient of a scalar field is a vector field that points in the direction of the greatest rate of increase of the scalar field, and whose magnitude is that rate of increase.
MatrixStore<N>	getHessian(Access1D<N> point)	The Hessian matrix or Hessian is a square matrix of second-order partial derivatives of a function.
MatrixStore<N>	getLinearFactors(boolean negated)
N	invoke(Access1D<N> arg)
PhysicalStore<N>	linear()
static <N extends Comparable<N>> LinearFunction<N>	wrap(PhysicalStore<N> coefficients)

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface MultiaryFunction

andThen

Methods inherited from interface MultiaryFunction.TwiceDifferentiable

toFirstOrderApproximation, toSecondOrderApproximation

Field Details

myCoefficients

private final MatrixStore<N extends Comparable<N>> myCoefficients

Constructor Details

LinearFunction

LinearFunction(MatrixStore<N> coefficients)

Method Details

factory

public static <N extends Comparable<N>>
LinearFunction.Factory<N> factory(PhysicalStore.Factory<N,?> factory)

wrap

public static <N extends Comparable<N>>
LinearFunction<N> wrap(PhysicalStore<N> coefficients)

arity

public int arity()

Specified by:

arity in interface MultiaryFunction<N extends Comparable<N>>

getGradient

public MatrixStore<N> getGradient(Access1D<N> point)

Description copied from interface: MultiaryFunction.TwiceDifferentiable

The gradient of a scalar field is a vector field that points in the direction of the greatest rate of increase of the scalar field, and whose magnitude is that rate of increase.

The Jacobian is a generalization of the gradient. Gradients are only defined on scalar-valued functions, but Jacobians are defined on vector- valued functions. When f is real-valued (i.e., f : Rn → R) the derivative Df(x) is a 1 × n matrix, i.e., it is a row vector. Its transpose is called the gradient of the function: ∇f(x) = Df(x)^T , which is a (column) vector, i.e., in Rn. Its components are the partial derivatives of f:

The first-order approximation of f at a point x ∈ int dom f can be expressed as (the affine function of z) f(z) = f(x) + ∇f(x)T (z − x).

Specified by:

getGradient in interface MultiaryFunction.TwiceDifferentiable<N extends Comparable<N>>

getHessian

public MatrixStore<N> getHessian(Access1D<N> point)

Description copied from interface: MultiaryFunction.TwiceDifferentiable

The Hessian matrix or Hessian is a square matrix of second-order partial derivatives of a function. It describes the local curvature of a function of many variables. The Hessian is the Jacobian of the gradient.

The second-order approximation of f, at or near x, is the quadratic function of z defined by f(z) = f(x) + ∇f(x)T (z − x) + (1/2)(z − x)T ∇2f(x)(z − x)

Specified by:

getHessian in interface MultiaryFunction.TwiceDifferentiable<N extends Comparable<N>>

getLinearFactors

public MatrixStore<N> getLinearFactors(boolean negated)

Specified by:

getLinearFactors in interface MultiaryFunction.TwiceDifferentiable<N extends Comparable<N>>

Returns:

The gradient at origin (0-vector), negated or not

invoke

public N invoke(Access1D<N> arg)

Specified by:

invoke in interface BasicFunction.PlainUnary<Access1D<N extends Comparable<N>>, N extends Comparable<N>>

Specified by:

invoke in interface MultiaryFunction<N extends Comparable<N>>

linear

public PhysicalStore<N> linear()

Specified by:

linear in interface MultiaryFunction.Linear<N extends Comparable<N>>

factory

PhysicalStore.Factory<N,?> factory()