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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math.optimization.general;
+
+import org.apache.commons.math.FunctionEvaluationException;
+
+/**
+ * This interface represents a preconditioner for differentiable scalar
+ * objective function optimizers.
+ * @version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $
+ * @since 2.0
+ */
+public interface Preconditioner {
+
+    /**
+     * Precondition a search direction.
+     * <p>
+     * The returned preconditioned search direction must be computed fast or
+     * the algorithm performances will drop drastically. A classical approach
+     * is to compute only the diagonal elements of the hessian and to divide
+     * the raw search direction by these elements if they are all positive.
+     * If at least one of them is negative, it is safer to return a clone of
+     * the raw search direction as if the hessian was the identity matrix. The
+     * rationale for this simplified choice is that a negative diagonal element
+     * means the current point is far from the optimum and preconditioning will
+     * not be efficient anyway in this case.
+     * </p>
+     * @param point current point at which the search direction was computed
+     * @param r raw search direction (i.e. opposite of the gradient)
+     * @return approximation of H<sup>-1</sup>r where H is the objective function hessian
+     * @exception FunctionEvaluationException if no cost can be computed for the parameters
+     * @exception IllegalArgumentException if point dimension is wrong
+     */
+    double[] precondition(double[] point, double[] r)
+        throws FunctionEvaluationException, IllegalArgumentException;
+
+}