Javadoc fixes.
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Phil Steitz
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a7294ccd79
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799a38a89f
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@ -95,7 +95,7 @@ public abstract class BaseAbstractUnivariateIntegrator implements UnivariateInte
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* the "reasonable value" varies widely for different algorithms. Users are
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* advised to use the default value supplied by the algorithm.</li>
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* </ul>
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* </p>
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*
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* @param relativeAccuracy relative accuracy of the result
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* @param absoluteAccuracy absolute accuracy of the result
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* @param minimalIterationCount minimum number of iterations
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@ -30,13 +30,14 @@ import org.apache.commons.math4.exception.TooManyEvaluationsException;
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public interface UnivariateIntegrator {
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/**
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* Get the actual relative accuracy.
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* Get the relative accuracy.
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*
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* @return the accuracy
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*/
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double getRelativeAccuracy();
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/**
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* Get the actual absolute accuracy.
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* Get the absolute accuracy.
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*
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* @return the accuracy
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*/
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@ -61,14 +62,14 @@ public interface UnivariateIntegrator {
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*
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* @param maxEval Maximum number of evaluations.
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* @param f the integrand function
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* @param min the min bound for the interval
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* @param min the lower bound for the interval
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* @param max the upper bound for the interval
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* @return the value of integral
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* @throws TooManyEvaluationsException if the maximum number of function
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* evaluations is exceeded.
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* evaluations is exceeded
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* @throws MaxCountExceededException if the maximum iteration count is exceeded
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* or the integrator detects convergence problems otherwise
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* @throws MathIllegalArgumentException if min > max or the endpoints do not
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* @throws MathIllegalArgumentException if {@code min > max} or the endpoints do not
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* satisfy the requirements specified by the integrator
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* @throws NullArgumentException if {@code f} is {@code null}.
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*/
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@ -79,12 +80,14 @@ public interface UnivariateIntegrator {
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/**
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* Get the number of function evaluations of the last run of the integrator.
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*
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* @return number of function evaluations
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*/
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int getEvaluations();
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/**
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* Get the number of iterations of the last run of the integrator.
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*
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* @return number of iterations
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*/
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int getIterations();
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@ -110,11 +110,9 @@ public class GaussIntegratorFactory {
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* The call to the
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* {@link SymmetricGaussIntegrator#integrate(org.apache.commons.math4.analysis.UnivariateFunction)
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* integrate} method will perform a weighted integration on the interval
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* {@code [-&inf;, +&inf;]}: the computed value is the improper integral of
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* <code>
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* e<sup>-x<sup>2</sup></sup> f(x)
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* </code>
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* where {@code f(x)} is the function passed to the
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* \([-\infty, +\infty]\): the computed value is the improper integral of
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* \(e^{-x^2}f(x)\)
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* where \(f(x)\) is the function passed to the
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* {@link SymmetricGaussIntegrator#integrate(org.apache.commons.math4.analysis.UnivariateFunction)
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* integrate} method.
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*
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@ -23,28 +23,27 @@ import org.apache.commons.math4.util.Pair;
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/**
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* Factory that creates a
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* <a href="http://en.wikipedia.org/wiki/Gauss-Hermite_quadrature">
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* Gauss-type quadrature rule using Hermite polynomials</a>
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* Gauss-type quadrature rule using Hermite polynomials</a>
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* of the first kind.
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* Such a quadrature rule allows the calculation of improper integrals
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* of a function
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* <code>
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* f(x) e<sup>-x<sup>2</sup></sup>
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* </code>
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* <br/>
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* <p>
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* \(f(x) e^{-x^2}\)
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* </p><p>
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* Recurrence relation and weights computation follow
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* <a href="http://en.wikipedia.org/wiki/Abramowitz_and_Stegun">
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* Abramowitz and Stegun, 1964</a>.
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* <br/>
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* The coefficients of the standard Hermite polynomials grow very rapidly;
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* in order to avoid overflows, each Hermite polynomial is normalized with
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* </p><p>
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* The coefficients of the standard Hermite polynomials grow very rapidly.
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* In order to avoid overflows, each Hermite polynomial is normalized with
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* respect to the underlying scalar product.
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* The initial interval for the application of the bisection method is
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* based on the roots of the previous Hermite polynomial (interlacing).
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* Upper and lower bounds of these roots are provided by
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* Upper and lower bounds of these roots are provided by </p>
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* <blockquote>
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* I. Krasikov,<br>
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* <em>Nonnegative quadratic forms and bounds on orthogonal polynomials</em>,<br>
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* Journal of Approximation theory <b>111</b>, 31-49<br>
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* I. Krasikov,
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* <em>Nonnegative quadratic forms and bounds on orthogonal polynomials</em>,
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* Journal of Approximation theory <b>111</b>, 31-49
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* </blockquote>
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*
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* @since 3.3
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@ -24,7 +24,7 @@ import org.apache.commons.math4.util.Pair;
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* In this implementation, the lower and upper bounds of the natural interval
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* of integration are -1 and 1, respectively.
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* The Legendre polynomials are evaluated using the recurrence relation
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* presented in <a href="http://en.wikipedia.org/wiki/Abramowitz_and_Stegun"
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* presented in <a href="http://en.wikipedia.org/wiki/Abramowitz_and_Stegun">
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* Abramowitz and Stegun, 1964</a>.
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*
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* @since 3.1
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@ -25,8 +25,8 @@ import org.apache.commons.math4.exception.NonMonotonicSequenceException;
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import org.apache.commons.math4.exception.NumberIsTooSmallException;
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/**
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* Implements the <a href="
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* http://mathworld.wolfram.com/NewtonsDividedDifferenceInterpolationFormula.html">
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* Implements the <a href=
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* "http://mathworld.wolfram.com/NewtonsDividedDifferenceInterpolationFormula.html">
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* Divided Difference Algorithm</a> for interpolation of real univariate
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* functions. For reference, see <b>Introduction to Numerical Analysis</b>,
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* ISBN 038795452X, chapter 2.
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@ -85,9 +85,10 @@ public class DividedDifferenceInterpolator
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* The divided difference array is defined recursively by <pre>
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* f[x0] = f(x0)
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* f[x0,x1,...,xk] = (f[x1,...,xk] - f[x0,...,x[k-1]]) / (xk - x0)
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* </pre></p>
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* </pre>
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* <p>
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* The computational complexity is O(N^2).</p>
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* The computational complexity is \(O(n^2)\) where \(n\) is the common
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* length of {@code x} and {@code y}.</p>
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*
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* @param x Interpolating points array.
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* @param y Interpolating values array.
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