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diff --git a/src/main/java/org/apache/commons/math3/optim/nonlinear/scalar/GradientMultivariateOptimizer.java b/src/main/java/org/apache/commons/math3/optim/nonlinear/scalar/GradientMultivariateOptimizer.java
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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.math3.optim.nonlinear.scalar;
+
+import org.apache.commons.math3.analysis.MultivariateVectorFunction;
+import org.apache.commons.math3.optim.ConvergenceChecker;
+import org.apache.commons.math3.optim.OptimizationData;
+import org.apache.commons.math3.optim.PointValuePair;
+import org.apache.commons.math3.exception.TooManyEvaluationsException;
+
+/**
+ * Base class for implementing optimizers for multivariate scalar
+ * differentiable functions.
+ * It contains boiler-plate code for dealing with gradient evaluation.
+ *
+ * @since 3.1
+ */
+public abstract class GradientMultivariateOptimizer
+    extends MultivariateOptimizer {
+    /**
+     * Gradient of the objective function.
+     */
+    private MultivariateVectorFunction gradient;
+
+    /**
+     * @param checker Convergence checker.
+     */
+    protected GradientMultivariateOptimizer(ConvergenceChecker<PointValuePair> checker) {
+        super(checker);
+    }
+
+    /**
+     * Compute the gradient vector.
+     *
+     * @param params Point at which the gradient must be evaluated.
+     * @return the gradient at the specified point.
+     */
+    protected double[] computeObjectiveGradient(final double[] params) {
+        return gradient.value(params);
+    }
+
+    /**
+     * {@inheritDoc}
+     *
+     * @param optData Optimization data. In addition to those documented in
+     * {@link MultivariateOptimizer#parseOptimizationData(OptimizationData[])
+     * MultivariateOptimizer}, this method will register the following data:
+     * <ul>
+     *  <li>{@link ObjectiveFunctionGradient}</li>
+     * </ul>
+     * @return {@inheritDoc}
+     * @throws TooManyEvaluationsException if the maximal number of
+     * evaluations (of the objective function) is exceeded.
+     */
+    @Override
+    public PointValuePair optimize(OptimizationData... optData)
+        throws TooManyEvaluationsException {
+        // Set up base class and perform computation.
+        return super.optimize(optData);
+    }
+
+    /**
+     * Scans the list of (required and optional) optimization data that
+     * characterize the problem.
+     *
+     * @param optData Optimization data.
+     * The following data will be looked for:
+     * <ul>
+     *  <li>{@link ObjectiveFunctionGradient}</li>
+     * </ul>
+     */
+    @Override
+    protected void parseOptimizationData(OptimizationData... optData) {
+        // Allow base class to register its own data.
+        super.parseOptimizationData(optData);
+
+        // The existing values (as set by the previous call) are reused if
+        // not provided in the argument list.
+        for (OptimizationData data : optData) {
+            if  (data instanceof ObjectiveFunctionGradient) {
+                gradient = ((ObjectiveFunctionGradient) data).getObjectiveFunctionGradient();
+                // If more data must be parsed, this statement _must_ be
+                // changed to "continue".
+                break;
+            }
+        }
+    }
+}