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diff --git a/src/main/java/org/apache/commons/math3/fitting/SimpleCurveFitter.java b/src/main/java/org/apache/commons/math3/fitting/SimpleCurveFitter.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.fitting;
+
+import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
+import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
+import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
+import org.apache.commons.math3.linear.DiagonalMatrix;
+
+import java.util.Collection;
+
+/**
+ * Fits points to a user-defined {@link ParametricUnivariateFunction function}.
+ *
+ * @since 3.4
+ */
+public class SimpleCurveFitter extends AbstractCurveFitter {
+    /** Function to fit. */
+    private final ParametricUnivariateFunction function;
+
+    /** Initial guess for the parameters. */
+    private final double[] initialGuess;
+
+    /** Maximum number of iterations of the optimization algorithm. */
+    private final int maxIter;
+
+    /**
+     * Contructor used by the factory methods.
+     *
+     * @param function Function to fit.
+     * @param initialGuess Initial guess. Cannot be {@code null}. Its length must be consistent with
+     *     the number of parameters of the {@code function} to fit.
+     * @param maxIter Maximum number of iterations of the optimization algorithm.
+     */
+    private SimpleCurveFitter(
+            ParametricUnivariateFunction function, double[] initialGuess, int maxIter) {
+        this.function = function;
+        this.initialGuess = initialGuess;
+        this.maxIter = maxIter;
+    }
+
+    /**
+     * Creates a curve fitter. The maximum number of iterations of the optimization algorithm is set
+     * to {@link Integer#MAX_VALUE}.
+     *
+     * @param f Function to fit.
+     * @param start Initial guess for the parameters. Cannot be {@code null}. Its length must be
+     *     consistent with the number of parameters of the function to fit.
+     * @return a curve fitter.
+     * @see #withStartPoint(double[])
+     * @see #withMaxIterations(int)
+     */
+    public static SimpleCurveFitter create(ParametricUnivariateFunction f, double[] start) {
+        return new SimpleCurveFitter(f, start, Integer.MAX_VALUE);
+    }
+
+    /**
+     * Configure the start point (initial guess).
+     *
+     * @param newStart new start point (initial guess)
+     * @return a new instance.
+     */
+    public SimpleCurveFitter withStartPoint(double[] newStart) {
+        return new SimpleCurveFitter(function, newStart.clone(), maxIter);
+    }
+
+    /**
+     * Configure the maximum number of iterations.
+     *
+     * @param newMaxIter maximum number of iterations
+     * @return a new instance.
+     */
+    public SimpleCurveFitter withMaxIterations(int newMaxIter) {
+        return new SimpleCurveFitter(function, initialGuess, newMaxIter);
+    }
+
+    /** {@inheritDoc} */
+    @Override
+    protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> observations) {
+        // Prepare least-squares problem.
+        final int len = observations.size();
+        final double[] target = new double[len];
+        final double[] weights = new double[len];
+
+        int count = 0;
+        for (WeightedObservedPoint obs : observations) {
+            target[count] = obs.getY();
+            weights[count] = obs.getWeight();
+            ++count;
+        }
+
+        final AbstractCurveFitter.TheoreticalValuesFunction model =
+                new AbstractCurveFitter.TheoreticalValuesFunction(function, observations);
+
+        // Create an optimizer for fitting the curve to the observed points.
+        return new LeastSquaresBuilder()
+                .maxEvaluations(Integer.MAX_VALUE)
+                .maxIterations(maxIter)
+                .start(initialGuess)
+                .target(target)
+                .weight(new DiagonalMatrix(weights))
+                .model(model.getModelFunction(), model.getModelFunctionJacobian())
+                .build();
+    }
+}