weka.core

## Class Optimization

• java.lang.Object
• weka.core.Optimization
• All Implemented Interfaces:
RevisionHandler, TechnicalInformationHandler

public abstract class Optimization
extends java.lang.Object
implements TechnicalInformationHandler, RevisionHandler
Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions. In this implementation we consider both the lower and higher bound constraints.

Here is the sketch of our searching strategy, and the detailed description of the algorithm can be found in the Appendix of Xin Xu's MSc thesis:

Initialize everything, incl. initial value, direction, etc.

LOOP (main algorithm):
1.1 Check all the bounds that are not "active" (i.e. binding variables) and compute the feasible step length to the bound for each of them
1.2 Pick up the least feasible step length, say \alpha, and set it as the upper bound of the current step length, i.e. 0<\lambda<=\alpha
1.3 Search for any possible step length<=\alpha that can result the "sufficient function decrease" (\alpha condition) AND "positive definite inverse Hessian" (\beta condition), if possible, using SAFEGUARDED polynomial interpolation. This step length is "safe" and thus is used to compute the next value of the free variables .
1.4 Fix the variable(s) that are newly bound to its constraint(s).

2. Check whether there is convergence of all variables or their gradients. If there is, check the possibilities to release any current bindings of the fixed variables to their bounds based on the "reliable" second-order Lagarange multipliers if available. If it's available and negative for one variable, then release it. If not available, use first-order Lagarange multiplier to test release. If there is any released variables, STOP the loop. Otherwise update the inverse of Hessian matrix and gradient for the newly released variables and CONTINUE LOOP.

3. Use BFGS formula to update the inverse of Hessian matrix. Note the already-fixed variables must have zeros in the corresponding entries in the inverse Hessian.

4. Compute the new (newton) search direction d=H^{-1}*g, where H^{-1} is the inverse Hessian and g is the Jacobian. Note that again, the already- fixed variables will have zero direction.

ENDLOOP

A typical usage of this class is to create your own subclass of this class and provide the objective function and gradients as follows:

 class MyOpt extends Optimization {
// Provide the objective function
protected double objectiveFunction(double[] x) {
// How to calculate your objective function...
// ...
}

// Provide the first derivatives
// How to calculate the gradient of the objective function...
// ...
}

// If possible, provide the index^{th} row of the Hessian matrix
protected double[] evaluateHessian(double[] x, int index) {
// How to calculate the index^th variable's second derivative
// ...
}
}

When it's the time to use it, in some routine(s) of other class...
 MyOpt opt = new MyOpt();

// Set up initial variable values and bound constraints
double[] x = new double[numVariables];
// Lower and upper bounds: 1st row is lower bounds, 2nd is upper
double[] constraints = new double[2][numVariables];
...

// Find the minimum, 200 iterations as default
x = opt.findArgmin(x, constraints);
while(x == null){  // 200 iterations are not enough
x = opt.getVarbValues();  // Try another 200 iterations
x = opt.findArgmin(x, constraints);
}

// The minimal function value
double minFunction = opt.getMinFunction();
...

It is recommended that Hessian values be provided so that the second-order Lagrangian multiplier estimate can be calcluated. However, if it is not provided, there is no need to override the evaluateHessian() function.

REFERENCES (see also the getTechnicalInformation() method):
The whole model algorithm is adapted from Chapter 5 and other related chapters in Gill, Murray and Wright(1981) "Practical Optimization", Academic Press. and Gill and Murray(1976) "Minimization Subject to Bounds on the Variables", NPL Report NAC72, while Chong and Zak(1996) "An Introduction to Optimization", John Wiley & Sons, Inc. provides us a brief but helpful introduction to the method.

Dennis and Schnabel(1983) "Numerical Methods for Unconstrained Optimization and Nonlinear Equations", Prentice-Hall Inc. and Press et al.(1992) "Numeric Recipe in C", Second Edition, Cambridge University Press. are consulted for the polynomial interpolation used in the line search implementation.

The Hessian modification in BFGS update uses Cholesky factorization and two rank-one modifications:
Bk+1 = Bk + (Gk*Gk')/(Gk'Dk) + (dGk*(dGk)'))/[alpha*(dGk)'*Dk].
where Gk is the gradient vector, Dk is the direction vector and alpha is the step rate.
This method is due to Gill, Golub, Murray and Saunders(1974) Methods for Modifying Matrix Factorizations'', Mathematics of Computation, Vol.28, No.126, pp 505-535.

Version:
$Revision: 1.9$
Author:
Xin Xu (xx5@cs.waikato.ac.nz)
getTechnicalInformation()
• ### Constructor Summary

Constructors
Constructor and Description
Optimization()
• ### Method Summary

All Methods
Modifier and Type Method and Description
double[] findArgmin(double[] initX, double[][] constraints)
Main algorithm.
double getMinFunction()
Get the minimal function value
TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
double[] getVarbValues()
Get the variable values.
double[] lnsrch(double[] xold, double[] gradient, double[] direct, double stpmax, boolean[] isFixed, double[][] nwsBounds, weka.core.Optimization.DynamicIntArray wsBdsIndx)
Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated.
void setDebug(boolean db)
Set whether in debug mode
void setMaxIteration(int it)
Set the maximal number of iterations in searching (Default 200)
static double[] solveTriangle(Matrix t, double[] b, boolean isLower, boolean[] isZero)
Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity
• ### Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Methods inherited from interface weka.core.RevisionHandler

getRevision
• ### Constructor Detail

• #### Optimization

public Optimization()
• ### Method Detail

• #### getTechnicalInformation

public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
Specified by:
getTechnicalInformation in interface TechnicalInformationHandler
Returns:
• #### getMinFunction

public double getMinFunction()
Get the minimal function value
Returns:
minimal function value found
• #### setMaxIteration

public void setMaxIteration(int it)
Set the maximal number of iterations in searching (Default 200)
Parameters:
it - the maximal number of iterations
• #### setDebug

public void setDebug(boolean db)
Set whether in debug mode
Parameters:
db - use debug or not
• #### getVarbValues

public double[] getVarbValues()
Get the variable values. Only needed when iterations exceeds the max threshold.
Returns:
the current variable values
• #### lnsrch

public double[] lnsrch(double[] xold,
double[] direct,
double stpmax,
boolean[] isFixed,
double[][] nwsBounds,
weka.core.Optimization.DynamicIntArray wsBdsIndx)
throws java.lang.Exception
Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated. Details see "Numerical Methods for Unconstrained Optimization and Nonlinear Equations". "Numeric Recipes in C" was also consulted.
Parameters:
xold - old x value
gradient - gradient at that point
direct - direction vector
stpmax - maximum step length
isFixed - indicating whether a variable has been fixed
nwsBounds - non-working set bounds. Means these variables are free and subject to the bound constraints in this step
wsBdsIndx - index of variables that has working-set bounds. Means these variables are already fixed and no longer subject to the constraints
Returns:
new value along direction p from xold, null if no step was taken
Throws:
java.lang.Exception - if an error occurs
• #### findArgmin

public double[] findArgmin(double[] initX,
double[][] constraints)
throws java.lang.Exception
Main algorithm. Descriptions see "Practical Optimization"
Parameters:
initX - initial point of x, assuming no value's on the bound!
constraints - the bound constraints of each variable constraints[0] is the lower bounds and constraints[1] is the upper bounds
Returns:
the solution of x, null if number of iterations not enough
Throws:
java.lang.Exception - if an error occurs
• #### solveTriangle

public static double[] solveTriangle(Matrix t,
double[] b,
boolean isLower,
boolean[] isZero)
Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity
Parameters:
t - the matrix T
b - the vector B
isLower - whether T is a lower or higher triangle matrix
isZero - which row(s) of T are not used when solving the equation. If it's null or all 'false', then every row is used.
Returns:
the solution of X