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commit befbe8f6e3f05c106a72016920f237928485f981
Author: Gilles Sadowski <gillese...@gmail.com>
AuthorDate: Wed Jul 14 12:49:28 2021 +0200
Javadoc.
---
.../nonlinear/scalar/noderiv/SimplexOptimizer.java | 71 ++++++++++------------
1 file changed, 32 insertions(+), 39 deletions(-)
diff --git
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
index f3cb927..81a04d6 100644
---
a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
+++
b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
@@ -33,29 +33,23 @@ import
org.apache.commons.math4.legacy.optim.nonlinear.scalar.MultivariateOptimi
* This class implements simplex-based direct search optimization.
*
* <p>
- * Direct search methods only use objective function values, they do
- * not need derivatives and don't either try to compute approximation
- * of the derivatives. According to a 1996 paper by Margaret H. Wright
- * (<a href="http://cm.bell-labs.com/cm/cs/doc/96/4-02.ps.gz";>Direct
- * Search Methods: Once Scorned, Now Respectable</a>), they are used
- * when either the computation of the derivative is impossible (noisy
- * functions, unpredictable discontinuities) or difficult (complexity,
- * computation cost). In the first cases, rather than an optimum, a
- * <em>not too bad</em> point is desired. In the latter cases, an
- * optimum is desired but cannot be reasonably found. In all cases
- * direct search methods can be useful.
- * </p>
- * <p>
- * Simplex-based direct search methods are based on comparison of
- * the objective function values at the vertices of a simplex (which is a
- * set of n+1 points in dimension n) that is updated by the algorithms
- * steps.
- * </p>
+ * Direct search methods only use objective function values, they do
+ * not need derivatives and don't either try to compute approximation
+ * of the derivatives. According to a 1996 paper by Margaret H. Wright
+ * (<a href="http://cm.bell-labs.com/cm/cs/doc/96/4-02.ps.gz";>Direct
+ * Search Methods: Once Scorned, Now Respectable</a>), they are used
+ * when either the computation of the derivative is impossible (noisy
+ * functions, unpredictable discontinuities) or difficult (complexity,
+ * computation cost). In the first cases, rather than an optimum, a
+ * <em>not too bad</em> point is desired. In the latter cases, an
+ * optimum is desired but cannot be reasonably found. In all cases
+ * direct search methods can be useful.
+ *
* <p>
- * The simplex update procedure ({@link NelderMeadTransform} or
- * {@link MultiDirectionalTransform}) must be passed to the
- * {@code optimize} method.
- * </p>
+ * Simplex-based direct search methods are based on comparison of
+ * the objective function values at the vertices of a simplex (which is a
+ * set of n+1 points in dimension n) that is updated by the algorithms
+ * steps.
*
* <p>
* In addition to those documented in
@@ -63,28 +57,27 @@ import
org.apache.commons.math4.legacy.optim.nonlinear.scalar.MultivariateOptimi
* an instance of this class will register the following data:
* <ul>
* <li>{@link Simplex}</li>
- * <li>{@link Simplex.TransformFactory}</li>
+ * <li>{@link Simplex.TransformFactory} (either {@link NelderMeadTransform}
+ * or {@link MultiDirectionalTransform})</li>
* </ul>
*
* <p>
- * Each call to {@code optimize} will re-use the start configuration of
- * the current simplex and move it such that its first vertex is at the
- * provided start point of the optimization.
- * If the {@code optimize} method is called to solve a different problem
- * and the number of parameters change, the simplex must be re-initialized
- * to one with the appropriate dimensions.
- * </p>
+ * Each call to {@code optimize} will re-use the start configuration of
+ * the current simplex and move it such that its first vertex is at the
+ * provided start point of the optimization.
+ * If the {@code optimize} method is called to solve a different problem
+ * and the number of parameters change, the simplex must be re-initialized
+ * to one with the appropriate dimensions.
+ *
* <p>
- * Convergence is checked by providing the <em>worst</em> points of
- * previous and current simplex to the convergence checker, not the best
- * ones.
- * </p>
+ * Convergence is considered achieved when <em>all</em> the simplex points
+ * have converged.
+ *
* <p>
- * This implementation does not directly support constrained optimization
- * with simple bounds.
- * The call to {@link #optimize(OptimizationData[]) optimize} will throw
- * {@link MathUnsupportedOperationException} if bounds are passed to it.
- * </p>
+ * This implementation does not directly support constrained optimization
+ * with simple bounds.
+ * The call to {@link #optimize(OptimizationData[]) optimize} will throw
+ * {@link MathUnsupportedOperationException} if bounds are passed to it.
*/
public class SimplexOptimizer extends MultivariateOptimizer {
/** Simplex update function factory. */
