/*
 * 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;
import org.apache.commons.math.MaxEvaluationsExceededException;
import org.apache.commons.math.MaxIterationsExceededException;
import org.apache.commons.math.analysis.DifferentiableMultivariateRealFunction;
import org.apache.commons.math.analysis.MultivariateVectorialFunction;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.RealConvergenceChecker;
import org.apache.commons.math.optimization.DifferentiableMultivariateRealOptimizer;
import org.apache.commons.math.optimization.RealPointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;

/**
 * Base class for implementing optimizers for multivariate scalar functions.
 * <p>
 * This base class handles the boilerplate methods associated to thresholds
 * settings, iterations and evaluations counting.
 * </p>
 * 
 * @version $Revision$ $Date$
 * @since 2.0
 */
public abstract class AbstractScalarDifferentiableOptimizer implements DifferentiableMultivariateRealOptimizer {

	/** Default maximal number of iterations allowed. */
	public static final int DEFAULT_MAX_ITERATIONS = 100;

	/** Maximal number of iterations allowed. */
	private int maxIterations;

	/** Number of iterations already performed. */
	private int iterations;

	/** Maximal number of evaluations allowed. */
	private int maxEvaluations;

	/** Number of evaluations already performed. */
	private int evaluations;

	/** Number of gradient evaluations. */
	private int gradientEvaluations;

	/** Convergence checker. */
	protected RealConvergenceChecker checker;

	/** Objective function. */
	private DifferentiableMultivariateRealFunction f;

	/** Objective function gradient. */
	private MultivariateVectorialFunction gradient;

	/** Type of optimization. */
	protected GoalType goalType;

	/** Current point set. */
	protected double[] point;

	/**
	 * Simple constructor with default settings.
	 * <p>
	 * The convergence check is set to a {@link SimpleScalarValueChecker} and
	 * the maximal number of evaluation is set to its default value.
	 * </p>
	 */
	protected AbstractScalarDifferentiableOptimizer() {
		setConvergenceChecker(new SimpleScalarValueChecker());
		setMaxIterations(DEFAULT_MAX_ITERATIONS);
		setMaxEvaluations(Integer.MAX_VALUE);
	}

	/** {@inheritDoc} */
	public void setMaxIterations(int maxIterations) {
		this.maxIterations = maxIterations;
	}

	/** {@inheritDoc} */
	public int getMaxIterations() {
		return maxIterations;
	}

	/** {@inheritDoc} */
	public int getIterations() {
		return iterations;
	}

	/** {@inheritDoc} */
	public void setMaxEvaluations(int maxEvaluations) {
		this.maxEvaluations = maxEvaluations;
	}

	/** {@inheritDoc} */
	public int getMaxEvaluations() {
		return maxEvaluations;
	}

	/** {@inheritDoc} */
	public int getEvaluations() {
		return evaluations;
	}

	/** {@inheritDoc} */
	public int getGradientEvaluations() {
		return gradientEvaluations;
	}

	/** {@inheritDoc} */
	public void setConvergenceChecker(RealConvergenceChecker checker) {
		this.checker = checker;
	}

	/** {@inheritDoc} */
	public RealConvergenceChecker getConvergenceChecker() {
		return checker;
	}

	/**
	 * Increment the iterations counter by 1.
	 * 
	 * @exception OptimizationException
	 *                if the maximal number of iterations is exceeded
	 */
	protected void incrementIterationsCounter() throws OptimizationException {
		if (++iterations > maxIterations) {
			throw new OptimizationException(new MaxIterationsExceededException(maxIterations));
		}
	}

	/**
	 * Compute the gradient vector.
	 * 
	 * @param point
	 *            point at which the gradient must be evaluated
	 * @return gradient at the specified point
	 * @exception FunctionEvaluationException
	 *                if the function gradient
	 */
	protected double[] computeObjectiveGradient(final double[] point) throws FunctionEvaluationException {
		++gradientEvaluations;
		return gradient.value(point);
	}

	/**
	 * Compute the objective function value.
	 * 
	 * @param point
	 *            point at which the objective function must be evaluated
	 * @return objective function value at specified point
	 * @exception FunctionEvaluationException
	 *                if the function cannot be evaluated or its dimension
	 *                doesn't match problem dimension or the maximal number of
	 *                iterations is exceeded
	 */
	protected double computeObjectiveValue(final double[] point) throws FunctionEvaluationException {
		if (++evaluations > maxEvaluations) {
			throw new FunctionEvaluationException(new MaxEvaluationsExceededException(maxEvaluations), point);
		}
		return f.value(point);
	}

	/** {@inheritDoc} */
	public RealPointValuePair optimize(final DifferentiableMultivariateRealFunction f, final GoalType goalType,
			final double[] startPoint)
			throws FunctionEvaluationException, OptimizationException, IllegalArgumentException {

		// reset counters
		iterations = 0;
		evaluations = 0;
		gradientEvaluations = 0;

		// store optimization problem characteristics
		this.f = f;
		gradient = f.gradient();
		this.goalType = goalType;
		point = startPoint.clone();

		return doOptimize();

	}

	/**
	 * Perform the bulk of optimization algorithm.
	 * 
	 * @return the point/value pair giving the optimal value for objective
	 *         function
	 * @exception FunctionEvaluationException
	 *                if the objective function throws one during the search
	 * @exception OptimizationException
	 *                if the algorithm failed to converge
	 * @exception IllegalArgumentException
	 *                if the start point dimension is wrong
	 */
	abstract protected RealPointValuePair doOptimize()
			throws FunctionEvaluationException, OptimizationException, IllegalArgumentException;

}