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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
The newest version!
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
*
* 1. Perform the line search using the directions for free variables
* 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
* protected double[] evaluateGradient(double[] x) {
* // How to calculate the gradient of the objective function...
* // ...
* }
*
* // If possible, provide the indexˆ{th} row of the Hessian matrix
* protected double[] evaluateHessian(double[] x, int index) {
* // How to calculate the indexˆth 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.
*
*
* @author Xin Xu ([email protected])
* @version $Revision: 15532 $
* @see #getTechnicalInformation()
*/
public abstract class Optimization implements TechnicalInformationHandler,
RevisionHandler {
protected double m_ALF = 1.0e-4;
protected double m_BETA = 0.9;
protected double m_TOLX = 1.0e-6;
protected double m_STPMX = 100.0;
protected int m_MAXITS = 200;
protected boolean m_Debug = false;
/** function value */
protected double m_f;
/** G'*p */
private double m_Slope;
/** Test if zero step in lnsrch */
protected boolean m_IsZeroStep = false;
/** Used when iteration overflow occurs */
protected double[] m_X;
/** Compute machine precision */
protected static double m_Epsilon, m_Zero;
static {
m_Epsilon = 1.0;
while (1.0 + m_Epsilon > 1.0) {
m_Epsilon /= 2.0;
}
m_Epsilon *= 2.0;
m_Zero = Math.sqrt(m_Epsilon);
}
/**
* 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.
*
* @return the technical information about this class
*/
@Override
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
TechnicalInformation additional;
result = new TechnicalInformation(Type.MASTERSTHESIS);
result.setValue(Field.AUTHOR, "Xin Xu");
result.setValue(Field.YEAR, "2003");
result.setValue(Field.TITLE,
"Statistical learning in multiple instance problem");
result.setValue(Field.SCHOOL, "University of Waikato");
result.setValue(Field.ADDRESS, "Hamilton, NZ");
result.setValue(Field.NOTE, "0657.594");
additional = result.add(Type.BOOK);
additional.setValue(Field.AUTHOR,
"P. E. Gill and W. Murray and M. H. Wright");
additional.setValue(Field.YEAR, "1981");
additional.setValue(Field.TITLE, "Practical Optimization");
additional.setValue(Field.PUBLISHER, "Academic Press");
additional.setValue(Field.ADDRESS, "London and New York");
additional = result.add(Type.TECHREPORT);
additional.setValue(Field.AUTHOR, "P. E. Gill and W. Murray");
additional.setValue(Field.YEAR, "1976");
additional.setValue(Field.TITLE,
"Minimization subject to bounds on the variables");
additional.setValue(Field.INSTITUTION, "National Physical Laboratory");
additional.setValue(Field.NUMBER, "NAC 72");
additional = result.add(Type.BOOK);
additional.setValue(Field.AUTHOR, "E. K. P. Chong and S. H. Zak");
additional.setValue(Field.YEAR, "1996");
additional.setValue(Field.TITLE, "An Introduction to Optimization");
additional.setValue(Field.PUBLISHER, "John Wiley and Sons");
additional.setValue(Field.ADDRESS, "New York");
additional = result.add(Type.BOOK);
additional.setValue(Field.AUTHOR, "J. E. Dennis and R. B. Schnabel");
additional.setValue(Field.YEAR, "1983");
additional
.setValue(Field.TITLE,
"Numerical Methods for Unconstrained Optimization and Nonlinear Equations");
additional.setValue(Field.PUBLISHER, "Prentice-Hall");
additional = result.add(Type.BOOK);
additional
.setValue(Field.AUTHOR,
"W. H. Press and B. P. Flannery and S. A. Teukolsky and W. T. Vetterling");
additional.setValue(Field.YEAR, "1992");
additional.setValue(Field.TITLE, "Numerical Recipes in C");
additional.setValue(Field.PUBLISHER, "Cambridge University Press");
additional.setValue(Field.EDITION, "Second");
additional = result.add(Type.ARTICLE);
additional.setValue(Field.AUTHOR,
"P. E. Gill and G. H. Golub and W. Murray and M. A. Saunders");
additional.setValue(Field.YEAR, "1974");
additional.setValue(Field.TITLE,
"Methods for modifying matrix factorizations");
additional.setValue(Field.JOURNAL, "Mathematics of Computation");
additional.setValue(Field.VOLUME, "28");
additional.setValue(Field.NUMBER, "126");
additional.setValue(Field.PAGES, "505-535");
return result;
}
/**
* Subclass should implement this procedure to evaluate objective function to
* be minimized
*
* @param x the variable values
* @return the objective function value
* @throws Exception if something goes wrong
*/
protected abstract double objectiveFunction(double[] x) throws Exception;
/**
* Subclass should implement this procedure to evaluate gradient of the
* objective function
*
* @param x the variable values
* @return the gradient vector
* @throws Exception if something goes wrong
*/
protected abstract double[] evaluateGradient(double[] x) throws Exception;
/**
* Subclass is recommended to override this procedure to evaluate second-order
* gradient of the objective function. If it's not provided, it returns null.
*
* @param x the variables
* @param index the row index in the Hessian matrix
* @return one row (the row #index) of the Hessian matrix, null as default
* @throws Exception if something goes wrong
*/
protected double[] evaluateHessian(double[] x, int index) throws Exception {
return null;
}
/**
* Get the minimal function value
*
* @return minimal function value found
*/
public double getMinFunction() {
return m_f;
}
/**
* Set the maximal number of iterations in searching (Default 200)
*
* @param it the maximal number of iterations
*/
public void setMaxIteration(int it) {
m_MAXITS = it;
}
/**
* Set whether in debug mode
*
* @param db use debug or not
*/
public void setDebug(boolean db) {
m_Debug = db;
}
/**
* Get the variable values. Only needed when iterations exceeds the max
* threshold.
*
* @return the current variable values
*/
public double[] getVarbValues() {
return m_X;
}
/**
* 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.
*
* @param xold old x value
* @param gradient gradient at that point
* @param direct direction vector
* @param stpmax maximum step length
* @param isFixed indicating whether a variable has been fixed
* @param nwsBounds non-working set bounds. Means these variables are free and
* subject to the bound constraints in this step
* @param wsBdsIndx index of variables that has working-set bounds. Means
* these variables are already fixed and no longer subject to the
* constraints
* @return new value along direction p from xold, null if no step was taken
* @throws Exception if an error occurs
*/
public double[] lnsrch(double[] xold, double[] gradient, double[] direct,
double stpmax, boolean[] isFixed, double[][] nwsBounds,
DynamicIntArray wsBdsIndx) throws Exception {
if (m_Debug) {
System.err.println("Machine precision is " + m_Epsilon
+ " and zero set to " + m_Zero);
}
int i, k, len = xold.length, fixedOne = -1; // idx of variable to be fixed
double alam, alamin; // lambda to be found, and its lower bound
// For convergence and bound test
double temp, test, alpha = Double.POSITIVE_INFINITY, fold = m_f, sum;
// For cubic interpolation
double a, alam2 = 0, b, disc = 0, maxalam = 1.0, rhs1, rhs2, tmplam;
double[] x = new double[len]; // New variable values
// Scale the step
for (sum = 0.0, i = 0; i < len; i++) {
if (!isFixed[i]) {
sum += direct[i] * direct[i];
}
}
sum = Math.sqrt(sum);
if (m_Debug) {
System.err.println("fold: " + Utils.doubleToString(fold, 10, 7) + "\n"
+ "sum: " + Utils.doubleToString(sum, 10, 7) + "\n" + "stpmax: "
+ Utils.doubleToString(stpmax, 10, 7));
}
if (sum > stpmax) {
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
direct[i] *= stpmax / sum;
}
}
} else {
maxalam = stpmax / sum;
}
// Compute initial rate of decrease, g'*d
m_Slope = 0.0;
for (i = 0; i < len; i++) {
x[i] = xold[i];
if (!isFixed[i]) {
m_Slope += gradient[i] * direct[i];
}
}
if (m_Debug) {
System.err
.print("slope: " + Utils.doubleToString(m_Slope, 10, 7) + "\n");
}
// Slope too small
if (Math.abs(m_Slope) <= m_Zero) {
if (m_Debug) {
System.err.println("Gradient and direction orthogonal -- "
+ "Min. found with current fixed variables"
+ " (or all variables fixed). Try to release"
+ " some variables now.");
}
return x;
}
// Err: slope > 0
if (m_Slope > m_Zero) {
if (m_Debug) {
for (int h = 0; h < x.length; h++) {
System.err.println(h + ": isFixed=" + isFixed[h] + ", x=" + x[h]
+ ", grad=" + gradient[h] + ", direct=" + direct[h]);
}
}
throw new Exception("g'*p positive! -- Try to debug from here: line 327.");
}
// Compute LAMBDAmin and upper bound of lambda--alpha
test = 0.0;
for (i = 0; i < len; i++) {
if (!isFixed[i]) {// No need for fixed variables
temp = Math.abs(direct[i]) / Math.max(Math.abs(x[i]), 1.0);
if (temp > test) {
test = temp;
}
}
}
if (test > m_Zero) {
alamin = m_TOLX / test;
} else {
if (m_Debug) {
System.err.println("Zero directions for all free variables -- "
+ "Min. found with current fixed variables"
+ " (or all variables fixed). Try to release"
+ " some variables now.");
}
return x;
}
// Check whether any non-working-set bounds are "binding"
for (i = 0; i < len; i++) {
if (!isFixed[i]) {// No need for fixed variables
double alpi;
if ((direct[i] < -m_Epsilon) && !Double.isNaN(nwsBounds[0][i])) {// Not
// feasible
alpi = (nwsBounds[0][i] - xold[i]) / direct[i];
if (alpi <= m_Zero) { // Zero
if (m_Debug) {
System.err.println("Fix variable " + i + " to lower bound "
+ nwsBounds[0][i] + " from value " + xold[i]);
}
x[i] = nwsBounds[0][i];
isFixed[i] = true; // Fix this variable
alpha = 0.0;
nwsBounds[0][i] = Double.NaN; // Add cons. to working set
wsBdsIndx.addElement(i);
} else if (alpha > alpi) { // Fix one variable in one iteration
alpha = alpi;
fixedOne = i;
}
} else if ((direct[i] > m_Epsilon) && !Double.isNaN(nwsBounds[1][i])) {// Not
// feasible
alpi = (nwsBounds[1][i] - xold[i]) / direct[i];
if (alpi <= m_Zero) { // Zero
if (m_Debug) {
System.err.println("Fix variable " + i + " to upper bound "
+ nwsBounds[1][i] + " from value " + xold[i]);
}
x[i] = nwsBounds[1][i];
isFixed[i] = true; // Fix this variable
alpha = 0.0;
nwsBounds[1][i] = Double.NaN; // Add cons. to working set
wsBdsIndx.addElement(i);
} else if (alpha > alpi) {
alpha = alpi;
fixedOne = i;
}
}
}
}
if (m_Debug) {
System.err.println("alamin: " + Utils.doubleToString(alamin, 10, 7));
System.err.println("alpha: " + Utils.doubleToString(alpha, 10, 7));
}
if (alpha <= m_Zero) { // Zero
m_IsZeroStep = true;
if (m_Debug) {
System.err.println("Alpha too small, try again");
}
return x;
}
alam = alpha; // Always try full feasible newton step
if (alam > 1.0) {
alam = 1.0;
}
// Iteration of one newton step, if necessary, backtracking is done
double initF = fold, // Initial function value
hi = alam, lo = alam, newSlope = 0, fhi = m_f, flo = m_f;// Variables used
// for beta
// condition
double[] newGrad; // Gradient on the new variable values
kloop: for (k = 0;; k++) {
if (m_Debug) {
System.err.println("\nLine search iteration: " + k);
}
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
x[i] = xold[i] + alam * direct[i]; // Compute xnew
if (!Double.isNaN(nwsBounds[0][i]) && (x[i] < nwsBounds[0][i])) {
x[i] = nwsBounds[0][i]; // Rounding error
} else if (!Double.isNaN(nwsBounds[1][i]) && (x[i] > nwsBounds[1][i])) {
x[i] = nwsBounds[1][i]; // Rounding error
}
}
}
m_f = objectiveFunction(x); // Compute fnew
if (Double.isNaN(m_f)) {
throw new Exception("Objective function value is NaN!");
}
while (Double.isInfinite(m_f)) { // Avoid infinity
if (m_Debug) {
System.err.println("Too large m_f. Shrink step by half.");
}
alam *= 0.5; // Shrink by half
if (alam <= m_Epsilon) {
if (m_Debug) {
System.err.println("Wrong starting points, change them!");
}
return x;
}
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
x[i] = xold[i] + alam * direct[i];
}
}
m_f = objectiveFunction(x);
if (Double.isNaN(m_f)) {
throw new Exception("Objective function value is NaN!");
}
initF = Double.POSITIVE_INFINITY;
}
if (m_Debug) {
System.err
.println("obj. function: " + Utils.doubleToString(m_f, 10, 7));
System.err.println("threshold: "
+ Utils.doubleToString(fold + m_ALF * alam * m_Slope, 10, 7));
}
if (m_f <= fold + m_ALF * alam * m_Slope) {// Alpha condition: sufficient
// function decrease
if (m_Debug) {
System.err
.println("Sufficient function decrease (alpha condition): ");
}
newGrad = evaluateGradient(x);
for (newSlope = 0.0, i = 0; i < len; i++) {
if (!isFixed[i]) {
newSlope += newGrad[i] * direct[i];
}
}
if (m_Debug) {
System.err.println("newSlope: " + newSlope);
}
if (newSlope >= m_BETA * m_Slope) { // Beta condition: ensure pos.
// defnty.
if (m_Debug) {
System.err.println("Increasing derivatives (beta condition): ");
}
if ((fixedOne != -1) && (alam >= alpha)) { // Has bounds and over
if (direct[fixedOne] > 0) {
x[fixedOne] = nwsBounds[1][fixedOne]; // Avoid rounding error
nwsBounds[1][fixedOne] = Double.NaN; // Add cons. to working set
} else {
x[fixedOne] = nwsBounds[0][fixedOne]; // Avoid rounding error
nwsBounds[0][fixedOne] = Double.NaN; // Add cons. to working set
}
if (m_Debug) {
System.err.println("Fix variable " + fixedOne + " to bound "
+ x[fixedOne] + " from value " + xold[fixedOne]);
}
isFixed[fixedOne] = true; // Fix the variable
wsBdsIndx.addElement(fixedOne);
}
return x;
} else if (k == 0) { // First time: increase alam
// Search for the smallest value not complying with alpha condition
double upper = Math.min(alpha, maxalam);
if (m_Debug) {
System.err.println("Alpha condition holds, increase alpha... ");
}
while (!((alam >= upper) || (m_f > fold + m_ALF * alam * m_Slope))) {
lo = alam;
flo = m_f;
alam *= 2.0;
if (alam >= upper) {
alam = upper;
}
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
x[i] = xold[i] + alam * direct[i];
}
}
m_f = objectiveFunction(x);
if (Double.isNaN(m_f)) {
throw new Exception("Objective function value is NaN!");
}
// Do not continue with the test for the beta condition if the alpha condition is no longer satisfied.
if (m_f > fold + m_ALF * alam * m_Slope) {
continue;
}
newGrad = evaluateGradient(x);
for (newSlope = 0.0, i = 0; i < len; i++) {
if (!isFixed[i]) {
newSlope += newGrad[i] * direct[i];
}
}
if (newSlope >= m_BETA * m_Slope) {
if (m_Debug) {
System.err
.println("Increasing derivatives (beta condition): \n"
+ "newSlope = " + Utils.doubleToString(newSlope, 10, 7));
}
if ((fixedOne != -1) && (alam >= alpha)) { // Has bounds and over
if (direct[fixedOne] > 0) {
x[fixedOne] = nwsBounds[1][fixedOne]; // Avoid rounding error
nwsBounds[1][fixedOne] = Double.NaN; // Add cons. to working
// set
} else {
x[fixedOne] = nwsBounds[0][fixedOne]; // Avoid rounding error
nwsBounds[0][fixedOne] = Double.NaN; // Add cons. to working
// set
}
if (m_Debug) {
System.err.println("Fix variable " + fixedOne + " to bound "
+ x[fixedOne] + " from value " + xold[fixedOne]);
}
isFixed[fixedOne] = true; // Fix the variable
wsBdsIndx.addElement(fixedOne);
}
return x;
}
}
hi = alam;
fhi = m_f;
break kloop;
} else {
if (m_Debug) {
System.err.println("Alpha condition holds.");
}
hi = alam2;
lo = alam;
flo = m_f;
break kloop;
}
} else if (alam < alamin) { // No feasible lambda found
if (initF < fold) {
alam = Math.min(1.0, alpha);
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
x[i] = xold[i] + alam * direct[i]; // Still take Alpha
}
}
if (m_Debug) {
System.err.println("No feasible lambda: still take" + " alpha="
+ alam);
}
if ((fixedOne != -1) && (alam >= alpha)) { // Has bounds and over
if (direct[fixedOne] > 0) {
x[fixedOne] = nwsBounds[1][fixedOne]; // Avoid rounding error
nwsBounds[1][fixedOne] = Double.NaN; // Add cons. to working set
} else {
x[fixedOne] = nwsBounds[0][fixedOne]; // Avoid rounding error
nwsBounds[0][fixedOne] = Double.NaN; // Add cons. to working set
}
if (m_Debug) {
System.err.println("Fix variable " + fixedOne + " to bound "
+ x[fixedOne] + " from value " + xold[fixedOne]);
}
isFixed[fixedOne] = true; // Fix the variable
wsBdsIndx.addElement(fixedOne);
}
} else { // Convergence on delta(x)
for (i = 0; i < len; i++) {
x[i] = xold[i];
}
m_f = fold;
if (m_Debug) {
System.err.println("Cannot find feasible lambda");
}
}
return x;
} else { // Backtracking by polynomial interpolation
if (k == 0) { // First time backtrack: quadratic interpolation
if (!Double.isInfinite(initF)) {
initF = m_f;
}
// lambda = -g'(0)/(2*g''(0))
tmplam = -0.5 * alam * m_Slope / ((m_f - fold) / alam - m_Slope);
} else { // Subsequent backtrack: cubic interpolation
rhs1 = m_f - fold - alam * m_Slope;
rhs2 = fhi - fold - alam2 * m_Slope;
a = (rhs1 / (alam * alam) - rhs2 / (alam2 * alam2)) / (alam - alam2);
b = (-alam2 * rhs1 / (alam * alam) + alam * rhs2 / (alam2 * alam2))
/ (alam - alam2);
if (a == 0.0) {
tmplam = -m_Slope / (2.0 * b);
} else {
disc = b * b - 3.0 * a * m_Slope;
if (disc < 0.0) {
disc = 0.0;
}
double numerator = -b + Math.sqrt(disc);
if (numerator >= Double.MAX_VALUE) {
numerator = Double.MAX_VALUE;
if (m_Debug) {
System.err
.print("-b+sqrt(disc) too large! Set it to MAX_VALUE.");
}
}
tmplam = numerator / (3.0 * a);
}
if (m_Debug) {
System.err.print("Cubic interpolation: \n" + "a: "
+ Utils.doubleToString(a, 10, 7) + "\n" + "b: "
+ Utils.doubleToString(b, 10, 7) + "\n" + "disc: "
+ Utils.doubleToString(disc, 10, 7) + "\n" + "tmplam: "
+ tmplam + "\n" + "alam: " + Utils.doubleToString(alam, 10, 7)
+ "\n");
}
if (tmplam > 0.5 * alam) {
tmplam = 0.5 * alam; // lambda <= 0.5*lambda_old
}
}
}
alam2 = alam;
fhi = m_f;
alam = Math.max(tmplam, 0.1 * alam); // lambda >= 0.1*lambda_old
if (alam > alpha) {
throw new Exception("Sth. wrong in lnsrch:"
+ "Lambda infeasible!(lambda=" + alam + ", alpha=" + alpha
+ ", upper=" + tmplam + "|"
+ (-alpha * m_Slope / (2.0 * ((m_f - fold) / alpha - m_Slope)))
+ ", m_f=" + m_f + ", fold=" + fold + ", slope=" + m_Slope);
}
} // Endfor(k=0;;k++)
// Quadratic interpolation between lamda values between lo and hi.
// If cannot find a value satisfying beta condition, use lo.
double ldiff = hi - lo, lincr;
if (m_Debug) {
System.err
.println("Last stage of searching for beta condition (alam between "
+ Utils.doubleToString(lo, 10, 7) + " and "
+ Utils.doubleToString(hi, 10, 7) + ")...\n"
+ "Quadratic Interpolation(QI):\n" + "Last newSlope = "
+ Utils.doubleToString(newSlope, 10, 7));
}
while ((newSlope < m_BETA * m_Slope) && (ldiff >= alamin)) {
lincr = -0.5 * newSlope * ldiff * ldiff / (fhi - flo - newSlope * ldiff);
if (m_Debug) {
System.err.println("fhi = " + fhi + "\n" + "flo = " + flo + "\n"
+ "ldiff = " + ldiff + "\n" + "lincr (using QI) = " + lincr + "\n");
}
if (lincr < 0.2 * ldiff) {
lincr = 0.2 * ldiff;
}
alam = lo + lincr;
if (alam >= hi) { // We cannot go beyond the bounds, so the best we can
// try is hi
alam = hi;
lincr = ldiff;
}
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
x[i] = xold[i] + alam * direct[i];
}
}
m_f = objectiveFunction(x);
if (Double.isNaN(m_f)) {
throw new Exception("Objective function value is NaN!");
}
if (m_f > fold + m_ALF * alam * m_Slope) {
// Alpha condition fails, shrink lambda_upper
ldiff = lincr;
fhi = m_f;
} else { // Alpha condition holds
newGrad = evaluateGradient(x);
for (newSlope = 0.0, i = 0; i < len; i++) {
if (!isFixed[i]) {
newSlope += newGrad[i] * direct[i];
}
}
if (newSlope < m_BETA * m_Slope) {
// Beta condition fails, shrink lambda_lower
lo = alam;
ldiff -= lincr;
flo = m_f;
}
}
}
if (newSlope < m_BETA * m_Slope) { // Cannot satisfy beta condition, take lo
if (m_Debug) {
System.err
.println("Beta condition cannot be satisfied, take alpha condition");
}
alam = lo;
for (i = 0; i < len; i++) {
if (!isFixed[i]) {
x[i] = xold[i] + alam * direct[i];
}
}
m_f = flo;
} else if (m_Debug) {
System.err.println("Both alpha and beta conditions are satisfied. alam="
+ Utils.doubleToString(alam, 10, 7));
}
if ((fixedOne != -1) && (alam >= alpha)) { // Has bounds and over
if (direct[fixedOne] > 0) {
x[fixedOne] = nwsBounds[1][fixedOne]; // Avoid rounding error
nwsBounds[1][fixedOne] = Double.NaN; // Add cons. to working set
} else {
x[fixedOne] = nwsBounds[0][fixedOne]; // Avoid rounding error
nwsBounds[0][fixedOne] = Double.NaN; // Add cons. to working set
}
if (m_Debug) {
System.err.println("Fix variable " + fixedOne + " to bound "
+ x[fixedOne] + " from value " + xold[fixedOne]);
}
isFixed[fixedOne] = true; // Fix the variable
wsBdsIndx.addElement(fixedOne);
}
return x;
}
/**
* Main algorithm. Descriptions see "Practical Optimization"
*
* @param initX initial point of x, assuming no value's on the bound!
* @param constraints the bound constraints of each variable constraints[0] is
* the lower bounds and constraints[1] is the upper bounds
* @return the solution of x, null if number of iterations not enough
* @throws Exception if an error occurs
*/
public double[] findArgmin(double[] initX, double[][] constraints)
throws Exception {
int l = initX.length;
// Initially all variables are free, all bounds are constraints of
// non-working-set constraints
boolean[] isFixed = new boolean[l];
double[][] nwsBounds = new double[2][l];
// Record indice of fixed variables, simply for efficiency
DynamicIntArray wsBdsIndx = new DynamicIntArray(constraints.length);
// Vectors used to record the variable indices to be freed
DynamicIntArray toFree = null, oldToFree = null;
// Initial value of obj. function, gradient and inverse of the Hessian
m_f = objectiveFunction(initX);
if (Double.isNaN(m_f)) {
throw new Exception("Objective function value is NaN!");
}
double sum = 0;
double[] grad = evaluateGradient(initX), oldGrad, oldX, deltaGrad = new double[l], deltaX = new double[l], direct = new double[l], x = new double[l];
Matrix L = new Matrix(l, l); // Lower triangle of Cholesky factor
double[] D = new double[l]; // Diagonal of Cholesky factor
for (int i = 0; i < l; i++) {
// L.setRow(i, new double[l]); Not necessary
L.set(i, i, 1.0);
D[i] = 1.0;
direct[i] = -grad[i];
sum += grad[i] * grad[i];
x[i] = initX[i];
nwsBounds[0][i] = constraints[0][i];
nwsBounds[1][i] = constraints[1][i];
isFixed[i] = false;
}
double stpmax = m_STPMX * Math.max(Math.sqrt(sum), l);
for (int step = 0; step < m_MAXITS; step++) {
if (m_Debug) {
System.err.println("\nIteration # " + step + ":");
}
// Try at most one feasible newton step, i.e. 0 test) {
test = tmp;
}
}
if (test < m_Zero) {
if (m_Debug) {
System.err.println("\nDeltaX converge: " + test);
}
finish = true;
}
// Check zero gradient
grad = evaluateGradient(x);
test = 0.0;
double denom = 0.0, dxSq = 0.0, dgSq = 0.0, newlyBounded = 0.0;
for (int g = 0; g < l; g++) {
if (!isFixed[g]) {
deltaGrad[g] = grad[g] - oldGrad[g];
// Calculate the denominators
denom += deltaX[g] * deltaGrad[g];
dxSq += deltaX[g] * deltaX[g];
dgSq += deltaGrad[g] * deltaGrad[g];
} else {
newlyBounded += deltaX[g] * (grad[g] - oldGrad[g]);
}
// Note: CANNOT use projected gradient for testing
// convergence because of newly bounded variables
double tmp = Math.abs(grad[g]) * Math.max(Math.abs(direct[g]), 1.0)
/ Math.max(Math.abs(m_f), 1.0);
if (tmp > test) {
test = tmp;
}
}
if (test < m_Zero) {
if (m_Debug) {
System.err.println("Gradient converge: " + test);
}
finish = true;
}
// dg'*dx could be < 0 using inexact lnsrch
if (m_Debug) {
System.err.println("dg'*dx=" + (denom + newlyBounded));
}
// dg'*dx = 0
if (Math.abs(denom + newlyBounded) < m_Zero) {
finish = true;
}
int size = wsBdsIndx.size();
boolean isUpdate = true; // Whether to update BFGS formula
// Converge: check whether release any current constraints
if (finish) {
if (m_Debug) {
System.err.println("Test any release possible ...");
}
if (toFree != null) {
oldToFree = (DynamicIntArray) toFree.copy();
}
toFree = new DynamicIntArray(wsBdsIndx.size());
for (int m = size - 1; m >= 0; m--) {
int index = wsBdsIndx.elementAt(m);
double[] hessian = evaluateHessian(x, index);
double deltaL = 0.0;
if (hessian != null) {
for (int mm = 0; mm < hessian.length; mm++) {
if (!isFixed[mm]) {
deltaL += hessian[mm] * direct[mm];
}
}
}
// First and second order Lagrangian multiplier estimate
// If user didn't provide Hessian, use first-order only
double L1, L2;
if (x[index] >= constraints[1][index]) {
L1 = -grad[index];
} else if (x[index] <= constraints[0][index]) {
L1 = grad[index];
} else {
throw new Exception("x[" + index + "] not fixed on the"
+ " bounds where it should have been!");
}
// L2 = L1 + deltaL
L2 = L1 + deltaL;
if (m_Debug) {
System.err.println("Variable " + index + ": Lagrangian=" + L1
+ "|" + L2);
}
// Check validity of Lagrangian multiplier estimate
boolean isConverge = (2.0 * Math.abs(deltaL)) < Math.min(
Math.abs(L1), Math.abs(L2));
if ((L1 * L2 > 0.0) && isConverge) { // Same sign and converge:
// valid
if (L2 < 0.0) {// Negative Lagrangian: feasible
toFree.addElement(index);
wsBdsIndx.removeElementAt(m);
finish = false; // Not optimal, cannot finish
}
}
// Although hardly happen, better check it
// If the first-order Lagrangian multiplier estimate is wrong,
// avoid zigzagging
if ((hessian == null) && (toFree != null)
&& toFree.equal(oldToFree)) {
finish = true;
}
}
if (finish) {// Min. found
if (m_Debug) {
System.err.println("Minimum found.");
}
m_f = objectiveFunction(x);
if (Double.isNaN(m_f)) {
throw new Exception("Objective function value is NaN!");
}
return x;
}
// Free some variables
for (int mmm = 0; mmm < toFree.size(); mmm++) {
int freeIndx = toFree.elementAt(mmm);
isFixed[freeIndx] = false; // Free this variable
if (x[freeIndx] <= constraints[0][freeIndx]) {// Lower bound
nwsBounds[0][freeIndx] = constraints[0][freeIndx];
if (m_Debug) {
System.err.println("Free variable " + freeIndx + " from bound "
+ nwsBounds[0][freeIndx]);
}
} else { // Upper bound
nwsBounds[1][freeIndx] = constraints[1][freeIndx];
if (m_Debug) {
System.err.println("Free variable " + freeIndx + " from bound "
+ nwsBounds[1][freeIndx]);
}
}
L.set(freeIndx, freeIndx, 1.0);
D[freeIndx] = 1.0;
isUpdate = false;
}
}
if (denom < Math
.max(m_Zero * Math.sqrt(dxSq) * Math.sqrt(dgSq), m_Zero)) {
if (m_Debug) {
System.err.println("dg'*dx negative!");
}
isUpdate = false; // Do not update
}
// If Hessian will be positive definite, update it
if (isUpdate) {
// modify once: dg*dg'/(dg'*dx)
double coeff = 1.0 / denom; // 1/(dg'*dx)
updateCholeskyFactor(L, D, deltaGrad, coeff, isFixed);
// modify twice: g*g'/(g'*p)
coeff = 1.0 / m_Slope; // 1/(g'*p)
updateCholeskyFactor(L, D, oldGrad, coeff, isFixed);
}
}
// Find new direction
Matrix LD = new Matrix(l, l); // L*D
double[] b = new double[l];
for (int k = 0; k < l; k++) {
if (!isFixed[k]) {
b[k] = -grad[k];
} else {
b[k] = 0.0;
}
for (int j = k; j < l; j++) { // Lower triangle
if (!isFixed[j] && !isFixed[k]) {
LD.set(j, k, L.get(j, k) * D[k]);
}
}
}
// Solve (LD)*y = -g, where y=L'*direct
double[] LDIR = solveTriangle(LD, b, true, isFixed);
LD = null;
for (int m = 0; m < LDIR.length; m++) {
if (Double.isNaN(LDIR[m])) {
throw new Exception("L*direct[" + m + "] is NaN!" + "|-g=" + b[m]
+ "|" + isFixed[m] + "|diag=" + D[m]);
}
}
// Solve L'*direct = y
direct = solveTriangle(L, LDIR, false, isFixed);
for (double element : direct) {
if (Double.isNaN(element)) {
throw new Exception("direct is NaN!");
}
}
// System.gc();
}
if (m_Debug) {
System.err.println("Cannot find minimum" + " -- too many interations!");
}
m_X = x;
return null;
}
/**
* 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
*
* @param t the matrix T
* @param b the vector B
* @param isLower whether T is a lower or higher triangle matrix
* @param 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.
* @return the solution of X
*/
public static double[] solveTriangle(Matrix t, double[] b, boolean isLower,
boolean[] isZero) {
int n = b.length;
double[] result = new double[n];
if (isZero == null) {
isZero = new boolean[n];
}
if (isLower) { // lower triangle, forward-substitution
int j = 0;
while ((j < n) && isZero[j]) {
result[j] = 0.0;
j++;
} // go to the first row
if (j < n) {
result[j] = b[j] / t.get(j, j);
for (; j < n; j++) {
if (!isZero[j]) {
double numerator = b[j];
for (int k = 0; k < j; k++) {
numerator -= t.get(j, k) * result[k];
}
result[j] = numerator / t.get(j, j);
} else {
result[j] = 0.0;
}
}
}
} else { // Upper triangle, back-substitution
int j = n - 1;
while ((j >= 0) && isZero[j]) {
result[j] = 0.0;
j--;
} // go to the last row
if (j >= 0) {
result[j] = b[j] / t.get(j, j);
for (; j >= 0; j--) {
if (!isZero[j]) {
double numerator = b[j];
for (int k = j + 1; k < n; k++) {
numerator -= t.get(k, j) * result[k];
}
result[j] = numerator / t.get(j, j);
} else {
result[j] = 0.0;
}
}
}
}
return result;
}
/**
* One rank update of the Cholesky factorization of B matrix in BFGS updates,
* i.e. B = LDL', and B_{new} = LDL' + coeff*(vv') where L is a unit lower
* triangle matrix and D is a diagonal matrix, and v is a vector.
* When coeff > 0, we use C1 algorithm, and otherwise we use C2 algorithm
* described in ``Methods for Modifying Matrix Factorizations''
*
* @param L the unit triangle matrix L
* @param D the diagonal matrix D
* @param v the update vector v
* @param coeff the coeffcient of update
* @param isFixed which variables are not to be updated
*/
protected void updateCholeskyFactor(Matrix L, double[] D, double[] v,
double coeff, boolean[] isFixed) throws Exception {
double t, p, b;
int n = v.length;
double[] vp = new double[n];
for (int i = 0; i < v.length; i++) {
if (!isFixed[i]) {
vp[i] = v[i];
} else {
vp[i] = 0.0;
}
}
if (coeff > 0.0) {
t = coeff;
for (int j = 0; j < n; j++) {
if (isFixed[j]) {
continue;
}
p = vp[j];
double d = D[j], dbarj = d + t * p * p;
D[j] = dbarj;
b = p * t / dbarj;
t *= d / dbarj;
for (int r = j + 1; r < n; r++) {
if (!isFixed[r]) {
double l = L.get(r, j);
vp[r] -= p * l;
L.set(r, j, l + b * vp[r]);
} else {
L.set(r, j, 0.0);
}
}
}
} else {
double[] P = solveTriangle(L, v, true, isFixed);
t = 0.0;
for (int i = 0; i < n; i++) {
if (!isFixed[i]) {
t += P[i] * P[i] / D[i];
}
}
double sqrt = 1.0 + coeff * t;
sqrt = (sqrt < 0.0) ? 0.0 : Math.sqrt(sqrt);
double alpha = coeff, sigma = coeff / (1.0 + sqrt), rho, theta;
for (int j = 0; j < n; j++) {
if (isFixed[j]) {
continue;
}
double d = D[j];
p = P[j] * P[j] / d;
theta = 1.0 + sigma * p;
t -= p;
if (t < 0.0) {
t = 0.0; // Rounding error
}
double plus = sigma * sigma * p * t;
if ((j < n - 1) && (plus <= m_Zero)) {
plus = m_Zero; // Avoid rounding error
}
rho = theta * theta + plus;
D[j] = rho * d;
if (Double.isNaN(D[j])) {
throw new Exception("d[" + j + "] NaN! P=" + P[j] + ",d=" + d + ",t="
+ t + ",p=" + p + ",sigma=" + sigma + ",sclar=" + coeff);
}
b = alpha * P[j] / (rho * d);
alpha /= rho;
rho = Math.sqrt(rho);
double sigmaOld = sigma;
sigma *= (1.0 + rho) / (rho * (theta + rho));
if ((j < n - 1) && (Double.isNaN(sigma) || Double.isInfinite(sigma))) {
throw new Exception("sigma NaN/Inf! rho=" + rho + ",theta=" + theta
+ ",P[" + j + "]=" + P[j] + ",p=" + p + ",d=" + d + ",t=" + t
+ ",oldsigma=" + sigmaOld);
}
for (int r = j + 1; r < n; r++) {
if (!isFixed[r]) {
double l = L.get(r, j);
vp[r] -= P[j] * l;
L.set(r, j, l + b * vp[r]);
} else {
L.set(r, j, 0.0);
}
}
}
}
}
/**
* Implements a simple dynamic array for ints.
*/
protected class DynamicIntArray implements RevisionHandler {
/** The int array. */
private int[] m_Objects;
/** The current size; */
private int m_Size = 0;
/** The capacity increment */
private int m_CapacityIncrement = 1;
/** The capacity multiplier. */
private int m_CapacityMultiplier = 2;
/**
* Constructs a vector with the given capacity.
*
* @param capacity the vector's initial capacity
*/
public DynamicIntArray(int capacity) {
m_Objects = new int[capacity];
}
/**
* Adds an element to this vector. Increases its capacity if its not large
* enough.
*
* @param element the element to add
*/
public final void addElement(int element) {
if (m_Size == m_Objects.length) {
int[] newObjects;
newObjects = new int[m_CapacityMultiplier
* (m_Objects.length + m_CapacityIncrement)];
System.arraycopy(m_Objects, 0, newObjects, 0, m_Size);
m_Objects = newObjects;
}
m_Objects[m_Size] = element;
m_Size++;
}
/**
* Produces a copy of this vector.
*
* @return the new vector
*/
public final Object copy() {
DynamicIntArray copy = new DynamicIntArray(m_Objects.length);
copy.m_Size = m_Size;
copy.m_CapacityIncrement = m_CapacityIncrement;
copy.m_CapacityMultiplier = m_CapacityMultiplier;
System.arraycopy(m_Objects, 0, copy.m_Objects, 0, m_Size);
return copy;
}
/**
* Returns the element at the given position.
*
* @param index the element's index
* @return the element with the given index
*/
public final int elementAt(int index) {
return m_Objects[index];
}
/**
* Check whether the two integer vectors equal to each other Two integer
* vectors are equal if all the elements are the same, regardless of the
* order of the elements
*
* @param b another integer vector
* @return whether they are equal
*/
private boolean equal(DynamicIntArray b) {
if ((b == null) || (size() != b.size())) {
return false;
}
int size = size();
// Only values matter, order does not matter
int[] sorta = Utils.sort(m_Objects), sortb = Utils.sort(b.m_Objects);
for (int j = 0; j < size; j++) {
if (m_Objects[sorta[j]] != b.m_Objects[sortb[j]]) {
return false;
}
}
return true;
}
/**
* Deletes an element from this vector.
*
* @param index the index of the element to be deleted
*/
public final void removeElementAt(int index) {
System.arraycopy(m_Objects, index + 1, m_Objects, index, m_Size - index
- 1);
m_Size--;
}
/**
* Removes all components from this vector and sets its size to zero.
*/
public final void removeAllElements() {
m_Objects = new int[m_Objects.length];
m_Size = 0;
}
/**
* Returns the vector's current size.
*
* @return the vector's current size
*/
public final int size() {
return m_Size;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 15532 $");
}
}
}