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BoundedVariableLeastSquares Class
Solve a linear least-squares problem with bounds on the variables.
Inheritance Hierarchy
SystemObject
  Imsl.MathBoundedVariableLeastSquares

Namespace: Imsl.Math
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0
Syntax
[SerializableAttribute]
public class BoundedVariableLeastSquares

The BoundedVariableLeastSquares type exposes the following members.

Constructors
  NameDescription
Public methodBoundedVariableLeastSquares
Construct a new BoundedVariableLeastSquares instance to solve Ax-b subject to bounds on the variables. Each upper bound must be greater than or equal to the corresponding lower bound.
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Methods
  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetDualSolution
Returns the dual solution vector, w.
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetSolution
Returns the solution to the problem.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodSolve
Find the solution x to the problem for the current constraints.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Properties
  NameDescription
Public propertyIterations
The number of iterations used to find the solution.
Public propertyMaxIterations
The maximum number of iterations.
Public propertyResidualNorm
The euclidean norm of the residual vector, \|Ax-b\|^2
            .
Public propertyTolerance
The internal tolerance used to determine the relative linear dependence of a column vector for a variable moved from its initial value.
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Remarks

BoundedVariableLeastSquares solves the least-squares problem

\min_x \| Ax-b\|^2
subject to the conditions
\alpha_k \le x_k \le \beta_k
for all k.

This algorithm is a generalization of NonNegativeLeastSquares, that solves the least-squares problem, Ax = b, subject to all x_j \ge 0. NonNegativeLeastSquares is based on the subroutine NNLS which appeared in Lawson and Hanson (1974). The additional work on bounded variable least squares was published in a later reprint (Lawson and Hanson, 1995).

See Also

Reference

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