SuanShu, a Java numerical and statistical library

com.numericalmethod.suanshu.optimization.constrained.constraint.linear
Class LinearGreaterThanConstraints

java.lang.Object
  extended by com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
      extended by com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearGreaterThanConstraints
All Implemented Interfaces:
Constraints, GreaterThanConstraints
Direct Known Subclasses:
LowerBoundConstraints

public class LinearGreaterThanConstraints
extends LinearConstraints
implements GreaterThanConstraints

This is a collection of linear greater-than-or-equal-to constraints.


 A * x ≥ b
 


Constructor Summary
LinearGreaterThanConstraints(Matrix A, Vector b)
          Construct a collection of linear greater-than or equal-to constraints.
 
Method Summary
 Vector getFeasibleInitialPoint()
          Given a collection of linear greater-than-or-equal-to constraints, find a feasible initial point that satisfy the constraints.
 Vector getFeasibleInitialPoint(LinearEqualityConstraints equal)
          Given a collection of linear greater-than-or-equal-to constraints as well as a collection of equality constraints, find a feasible initial point that satisfy the constraints.
 LinearLessThanConstraints toLessThanConstraints()
          Convert the greater-than or equal-to constraints to less-than or equal-to constraints.
 
Methods inherited from class com.numericalmethod.suanshu.optimization.constrained.constraint.linear.LinearConstraints
A, b, concat, dimension, getActiveConstraints, getActiveRows, getConstraints, size, toString
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
 
Methods inherited from interface com.numericalmethod.suanshu.optimization.constrained.constraint.Constraints
dimension, getConstraints, size
 

Constructor Detail

LinearGreaterThanConstraints

public LinearGreaterThanConstraints(Matrix A,
                                    Vector b)
Construct a collection of linear greater-than or equal-to constraints.

Parameters:
A - the inequality coefficients
b - the inequality values
Method Detail

toLessThanConstraints

public LinearLessThanConstraints toLessThanConstraints()
Description copied from interface: GreaterThanConstraints
Convert the greater-than or equal-to constraints to less-than or equal-to constraints.

Specified by:
toLessThanConstraints in interface GreaterThanConstraints
Returns:
the equivalent less-than or equal-to constraints

getFeasibleInitialPoint

public Vector getFeasibleInitialPoint(LinearEqualityConstraints equal)
Given a collection of linear greater-than-or-equal-to constraints as well as a collection of equality constraints, find a feasible initial point that satisfy the constraints. This implementation solves eq. 11.25 in the reference. The first (n-1) entries consist of a feasible initial point. The last entry is the single point perturbation.

Parameters:
equal - a collection of linear equality constraints
Returns:
a feasible initial point, and the single point perturbation (in one vector)
See Also:
  • "Jorge Nocedal, Stephen Wright, "p. 473," Numerical Optimization."
  • "Andreas Antoniou, Wu-Sheng Lu, "Eq 11.25, Quadratic and Convex Programming," Practical Optimization: Algorithms and Engineering Applications."
  • http://www.mathworks.com/help/toolbox/optim/ug/brnox7l.html (initialization)

getFeasibleInitialPoint

public Vector getFeasibleInitialPoint()
Given a collection of linear greater-than-or-equal-to constraints, find a feasible initial point that satisfy the constraints. This implementation solves eq. 11.25 in the reference. The first (n-1) entries consist of a feasible initial point. The last entry is the single point perturbation.

Returns:
a feasible initial point, and the single point perturbation (in one vector)
See Also:
"Andreas Antoniou, Wu-Sheng Lu, "Eq 11.25, Quadratic and Convex Programming," Practical Optimization: Algorithms and Engineering Applications."

SuanShu, a Java numerical and statistical library

Copyright © 2012 Numerical Method Inc. Ltd. All Rights Reserved.