com.imsl.math
Class BoundedLeastSquares

java.lang.Object
  com.imsl.math.BoundedLeastSquares

All Implemented Interfaces:

Cloneable, Serializable

public class BoundedLeastSquares

extends Object

implements Serializable, Cloneable

Solves a nonlinear least-squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm.

Class BoundedLeastSquares uses a modified Levenberg-Marquardt method and an active set strategy to solve nonlinear least-squares problems subject to simple bounds on the variables. The problem is stated as follows:

subject to

where , , and is the i-th component function of F(x). From a given starting point, an active set IA, which contains the indices of the variables at their bounds, is built. A variable is called a "free variable" if it is not in the active set. The routine then computes the search direction for the free variables according to the formula

where is the Levenberg-Marquardt parameter, F = F(x), and J is the Jacobian with respect to the free variables. The search direction for the variables in IA is set to zero. The trust region approach discussed by Dennis and Schnabel (1983) is used to find the new point. Finally, the optimality conditions are checked. The conditions are:

where is a gradient tolerance. This process is repeated until the optimality criterion is achieved.

The active set is changed only when a free variable hits its bounds during an iteration or the optimality condition is met for the free variables but not for all variables in IA, the active set. In the latter case, a variable that violates the optimality condition will be dropped out of IA. For more details on the Levenberg-Marquardt method, see Levenberg (1944) or Marquardt (1963). For more detail on the active set strategy, see Gill and Murray (1976).

See Also:

Example 1, Example 2, Serialized Form

Nested Class Summary

static class	BoundedLeastSquares.FalseConvergenceException False convergence - The iterates appear to be converging to a noncritical point.
static interface	BoundedLeastSquares.Function Public interface for the user-supplied function to evaluate the function that defines the least-squares problem.
static interface	BoundedLeastSquares.Jacobian Public interface for the user-supplied function to compute the Jacobian.

Constructor Summary

BoundedLeastSquares(BoundedLeastSquares.Function function, int mFunctions, int nVariables, int boundType, double[] lowerBound, double[] upperBound)
Constructor for BoundedLeastSquares.

Method Summary

double[][]	getJacobian() Returns the Jacobian at the approximate solution.
double[]	getResiduals() Returns the residuals at the approximate solution.
double[]	getSolution() Returns the solution.
void	setAbsoluteFcnTol(double absoluteFcnTol) Sets the absolute function tolerance.
void	setDiagonalScalingMatrix(double[] diagonalScalingMatrix) Sets the diagonal scaling matrix for the functions.
void	setGoodDigit(int goodDigit) Sets the number of good digits in the function.
void	setGradientTol(double gradientTol) Sets the scaled gradient tolerance.
void	setGuess(double[] guess) Sets the initial guess of the solution.
void	setInternalScale() Sets the internal variable scaling option.
void	setJacobian(BoundedLeastSquares.Jacobian jacobian) Sets the Jacobian.
void	setMaximumFunctionEvals(int evaluations) Sets the maximum number of function evaluations.
void	setMaximumIteration(int iterations) Sets the maximum number of iterations.
void	setMaximumJacobianEvals(int evaluations) Sets the maximum number of Jacobian evaluations.
void	setMaximumStepSize(double stepSize) Sets the maximum allowable step size.
void	setRelativeFcnTol(double relativeFcnTol) Sets the relative function tolerance.
void	setScaledStepTol(double scaledStepTol) Sets the scaled step tolerance.
void	setScalingVector(double[] scalingVector) Sets the scaling vector for the variables.
void	setTrustRegion(double trustRegion) Sets the size of initial trust region radius.
void	solve() Solves a nonlinear least-squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm.

Methods inherited from class java.lang.Object

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

Constructor Detail

BoundedLeastSquares

public BoundedLeastSquares(BoundedLeastSquares.Function function,
                           int mFunctions,
                           int nVariables,
                           int boundType,
                           double[] lowerBound,
                           double[] upperBound)

Constructor for BoundedLeastSquares.

Parameters:

function - a Function object, user-supplied function to evaluate the function

mFunctions - a int scalar containing the number of functions

nVariables - a int scalar containing the number of variables

boundType - a int scalar containing the types of bounds on the variable

boundType	Action
0	User will supply all the bounds.
1	All variables are nonnegative.
2	All variables are nonpositive.
3	User supplies only the bounds on first variable, all other variables will have the same bounds.

lowerBound - a double array containing the lower bounds on the variables

upperBound - a double array containing the upper bounds on the variables

Throws:

IllegalArgumentException - is thrown if the dimensions of mFunctions, nVariables, boundType, lowerBound.length and upperBound.length are not consistent

Method Detail

getJacobian

public double[][] getJacobian()

Returns the Jacobian at the approximate solution.

Returns:

a mFunctions x nVariables double matrix containing the Jacobian at the approximate solution

getResiduals

public double[] getResiduals()

Returns the residuals at the approximate solution.

Returns:

a double array containing the residuals at the approximate solution

getSolution

public double[] getSolution()

Returns the solution.

Returns:

a double array containing the computed solution

setAbsoluteFcnTol

public void setAbsoluteFcnTol(double absoluteFcnTol)

Sets the absolute function tolerance. If this member function is not called, a value of Math.max(1.0e-10, Math.pow(2.2204460492503131e-16, 2.0/3.0)), is used.

Parameters:

absoluteFcnTol - a double scalar containing the absolute function tolerance

setDiagonalScalingMatrix

public void setDiagonalScalingMatrix(double[] diagonalScalingMatrix)

Sets the diagonal scaling matrix for the functions. The i-th component of the array is a positive scalar specifying the reciprocal magnitude of the i-th component function of the problem. If this member function is not called, an initial scaling of 1.0 is used.

Parameters:

diagonalScalingMatrix - a double array containing the diagonal scaling for the functions

setGoodDigit

public void setGoodDigit(int goodDigit)

Sets the number of good digits in the function. If this member function is not called, a value of (int)(-Sfun.log10(2.2204460492503131e-16) + 0.1e0) is used.

Parameters:

goodDigit - a int scalar containing the number of good digits

setGradientTol

public void setGradientTol(double gradientTol)

Sets the scaled gradient tolerance. If this member function is not called, a value of Math.pow(2.2204460492503131e-16, 1.0e0/3.0e0) is used.

Parameters:

gradientTol - a double scalar containing the scaled gradient tolerance

setGuess

public void setGuess(double[] guess)

Sets the initial guess of the solution. If this member function is not called, an initial scaling of 1.0 is used.

Parameters:

guess - a double array containing an initial guess

setInternalScale

public void setInternalScale()

Sets the internal variable scaling option. With this option, scaling for the variables is set internally.

setJacobian

public void setJacobian(BoundedLeastSquares.Jacobian jacobian)

Sets the Jacobian.

Parameters:

jacobian - a Jacobian object to compute the Jacobian.

setMaximumFunctionEvals

public void setMaximumFunctionEvals(int evaluations)

Sets the maximum number of function evaluations. If this member function is not called, a value of 400 is used.

Parameters:

evaluations - a int scalar containing the maximum number of function evaluations

setMaximumIteration

public void setMaximumIteration(int iterations)

Sets the maximum number of iterations. If this member function is not called, a value of 100 is used.

Parameters:

iterations - a int scalar containing the maximum number of iterations

setMaximumJacobianEvals

public void setMaximumJacobianEvals(int evaluations)

Sets the maximum number of Jacobian evaluations. If this member function is not called, a value of 400 is used.

Parameters:

evaluations - a int scalar containing the maximum number of Jacobian evaluations

setMaximumStepSize

public void setMaximumStepSize(double stepSize)

Sets the maximum allowable step size.

Parameters:

stepSize - a double scalar containing the maximum allowable step size

setRelativeFcnTol

public void setRelativeFcnTol(double relativeFcnTol)

Sets the relative function tolerance. If this member function is not called, a value of Math.pow(2.2204460492503131e-16, 2.0e0/3.0e0) is used.

Parameters:

relativeFcnTol - a double scalar containing the relative function tolerance

setScaledStepTol

public void setScaledStepTol(double scaledStepTol)

Sets the scaled step tolerance. If this member function is not called, a value of Math.max(1.0e-10, Math.pow(2.2204460492503131e-16, 2.0e0/3.0e0) is used.

Parameters:

scaledStepTol - a double scalar containing the scaled step tolerance

setScalingVector

public void setScalingVector(double[] scalingVector)

Sets the scaling vector for the variables. If this member function is not called, an initial scaling of 1.0 is used.

Parameters:

scalingVector - a double array containing the scaling vector for the variables

setTrustRegion

public void setTrustRegion(double trustRegion)

Sets the size of initial trust region radius. If this member function is not called, the value is based on the initial scaled Cauchy step.

Parameters:

trustRegion - a double scalar containing the initial trust region radius

solve

public final void solve()
                 throws BoundedLeastSquares.FalseConvergenceException

Solves a nonlinear least-squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm.

Throws:

BoundedLeastSquares.FalseConvergenceException