C++ Problem::currentValue方法代码示例

    void FireflyAlgorithm::startState(Problem &P, const EndCriteria &endCriteria) {
        N_ = P.currentValue().size();
        x_.reserve(M_);
        xI_.reserve(M_);
        xRW_.reserve(M_);
        values_.reserve(M_);
        uX_ = P.constraint().upperBound(P.currentValue());
        lX_ = P.constraint().lowerBound(P.currentValue());
        Array bounds = uX_ - lX_;

        //Random initialization is done by Sobol sequence
        SobolRsg sobol(N_);

        //Prepare containers
        for (Size i = 0; i < M_; i++) {
            const SobolRsg::sample_type::value_type &sample = sobol.nextSequence().value;
            x_.push_back(Array(N_, 0.0));
            xI_.push_back(Array(N_, 0.0));
            xRW_.push_back(Array(N_, 0.0));
            Array& x = x_.back();
            for (Size j = 0; j < N_; j++) {
                //Assign X=lb+(ub-lb)*random
                x[j] = lX_[j] + bounds[j] * sample[j];
            }
            //Evaluate point
            values_.push_back(std::make_pair(P.value(x), i));
        }

        //init intensity & randomWalk
        intensity_->init(this);
        randomWalk_->init(this);
    }

    void DifferentialEvolution::fillInitialPopulation(
                                          std::vector<Candidate> & population,
                                          const Problem& p) const {

        // use initial values provided by the user
        population.front().values = p.currentValue();
        population.front().cost = p.costFunction().value(population.front().values);
        // rest of the initial population is random
        for (Size j = 1; j < population.size(); ++j) {
            for (Size i = 0; i < p.currentValue().size(); ++i) {
                Real l = lowerBound_[i], u = upperBound_[i];
                population[j].values[i] = l + (u-l)*rng_.nextReal();
            }
            population[j].cost = p.costFunction().value(population[j].values);
        }
    }

    EndCriteria::Type DifferentialEvolution::minimize(Problem& p, const EndCriteria& endCriteria) {
        EndCriteria::Type ecType;

        upperBound_ = p.constraint().upperBound(p.currentValue());
        lowerBound_ = p.constraint().lowerBound(p.currentValue());
        currGenSizeWeights_ = Array(configuration().populationMembers,
                                    configuration().stepsizeWeight);
        currGenCrossover_ = Array(configuration().populationMembers,
                                  configuration().crossoverProbability);

        std::vector<Candidate> population(configuration().populationMembers,
                                          Candidate(p.currentValue().size()));
        fillInitialPopulation(population, p);

        std::partial_sort(population.begin(), population.begin() + 1, population.end(),
                          sort_by_cost());
        bestMemberEver_ = population.front();
        Real fxOld = population.front().cost;
        Size iteration = 0, stationaryPointIteration = 0;

        // main loop - calculate consecutive emerging populations
        while (!endCriteria.checkMaxIterations(iteration++, ecType)) {
            calculateNextGeneration(population, p.costFunction());
            std::partial_sort(population.begin(), population.begin() + 1, population.end(),
                              sort_by_cost());
            if (population.front().cost < bestMemberEver_.cost)
                bestMemberEver_ = population.front();
            Real fxNew = population.front().cost;
            if (endCriteria.checkStationaryFunctionValue(fxOld, fxNew, stationaryPointIteration,
                                                         ecType))
                break;
            fxOld = fxNew;
        };
        p.setCurrentValue(bestMemberEver_.values);
        p.setFunctionValue(bestMemberEver_.cost);
        return ecType;
    }

    Real GoldsteinLineSearch::operator()(Problem& P, 
                                         EndCriteria::Type& ecType, 
                                         const EndCriteria& endCriteria, 
                                         const Real t_ini) 
    {
        Constraint& constraint = P.constraint();
        succeed_=true;
        bool maxIter = false;
        Real /*qtold,*/ t = t_ini; // see below, this is never used ?
        Size loopNumber = 0;

        Real q0 = P.functionValue();
        Real qp0 = P.gradientNormValue();
        
        Real tl = 0.0;
        Real tr = 0.0;

        qt_ = q0;
        qpt_ = (gradient_.empty()) ? qp0 : -DotProduct(gradient_,searchDirection_);

        // Initialize gradient
        gradient_ = Array(P.currentValue().size());
        // Compute new point
        xtd_ = P.currentValue();
        t = update(xtd_, searchDirection_, t, constraint);
        // Compute function value at the new point
        qt_ = P.value (xtd_);

        while ((qt_ - q0) < -beta_*t*qpt_ || (qt_ - q0) > -alpha_*t*qpt_) {
            if ((qt_ - q0) > -alpha_*t*qpt_)
                tr = t;
            else
                tl = t;
            ++loopNumber;
            
            // calculate the new step
            if (close_enough(tr, 0.0))
                t *= extrapolation_;
            else
                t = (tl + tr) / 2.0;

            // Store old value of the function
            // qtold = qt_; // this is never used ?
            // New point value
            xtd_ = P.currentValue();
            t = update(xtd_, searchDirection_, t, constraint);

            // Compute function value at the new point
            qt_ = P.value (xtd_);
            P.gradient (gradient_, xtd_);
            // and it squared norm
            maxIter = endCriteria.checkMaxIterations(loopNumber, ecType);

            if (maxIter)
                break;
        }

        if (maxIter)
            succeed_ = false;

        // Compute new gradient
        P.gradient(gradient_, xtd_);
        // and it squared norm
        qpt_ = DotProduct(gradient_, gradient_);

        // Return new step value
        return t;
    }

    EndCriteria::Type LevenbergMarquardt::minimize(Problem& P,
                                                   const EndCriteria& endCriteria) {
        EndCriteria::Type ecType = EndCriteria::None;
        P.reset();
        Array x_ = P.currentValue();
        currentProblem_ = &P;
        initCostValues_ = P.costFunction().values(x_);
        int m = initCostValues_.size();
        int n = x_.size();
        boost::scoped_array<double> xx(new double[n]);
        std::copy(x_.begin(), x_.end(), xx.get());
        boost::scoped_array<double> fvec(new double[m]);
        boost::scoped_array<double> diag(new double[n]);
        int mode = 1;
        double factor = 1;
        int nprint = 0;
        int info = 0;
        int nfev =0;
        boost::scoped_array<double> fjac(new double[m*n]);
        int ldfjac = m;
        boost::scoped_array<int> ipvt(new int[n]);
        boost::scoped_array<double> qtf(new double[n]);
        boost::scoped_array<double> wa1(new double[n]);
        boost::scoped_array<double> wa2(new double[n]);
        boost::scoped_array<double> wa3(new double[n]);
        boost::scoped_array<double> wa4(new double[m]);
        // requirements; check here to get more detailed error messages.
        QL_REQUIRE(n > 0, "no variables given");
        QL_REQUIRE(m >= n,
                   "less functions (" << m <<
                   ") than available variables (" << n << ")");
        QL_REQUIRE(endCriteria.functionEpsilon() >= 0.0,
                   "negative f tolerance");
        QL_REQUIRE(xtol_ >= 0.0, "negative x tolerance");
        QL_REQUIRE(gtol_ >= 0.0, "negative g tolerance");
        QL_REQUIRE(endCriteria.maxIterations() > 0,
                   "null number of evaluations");

        // call lmdif to minimize the sum of the squares of m functions
        // in n variables by the Levenberg-Marquardt algorithm.
        MINPACK::LmdifCostFunction lmdifCostFunction = 
            boost::bind(&LevenbergMarquardt::fcn, this, _1, _2, _3, _4, _5);
        MINPACK::lmdif(m, n, xx.get(), fvec.get(),
                       static_cast<double>(endCriteria.functionEpsilon()),
                       static_cast<double>(xtol_),
                       static_cast<double>(gtol_),
                       static_cast<int>(endCriteria.maxIterations()),
                       static_cast<double>(epsfcn_),
                       diag.get(), mode, factor,
                       nprint, &info, &nfev, fjac.get(),
                       ldfjac, ipvt.get(), qtf.get(),
                       wa1.get(), wa2.get(), wa3.get(), wa4.get(),
                       lmdifCostFunction);
        info_ = info;
        // check requirements & endCriteria evaluation
        QL_REQUIRE(info != 0, "MINPACK: improper input parameters");
        //QL_REQUIRE(info != 6, "MINPACK: ftol is too small. no further "
        //                               "reduction in the sum of squares "
        //                               "is possible.");
        if (info != 6) ecType = QuantLib::EndCriteria::StationaryFunctionValue;
        //QL_REQUIRE(info != 5, "MINPACK: number of calls to fcn has "
        //                               "reached or exceeded maxfev.");
        endCriteria.checkMaxIterations(nfev, ecType);
        QL_REQUIRE(info != 7, "MINPACK: xtol is too small. no further "
                                       "improvement in the approximate "
                                       "solution x is possible.");
        QL_REQUIRE(info != 8, "MINPACK: gtol is too small. fvec is "
                                       "orthogonal to the columns of the "
                                       "jacobian to machine precision.");
        // set problem
        std::copy(xx.get(), xx.get()+n, x_.begin());
        P.setCurrentValue(x_);
        P.setFunctionValue(P.costFunction().value(x_));
        
        return ecType;
    }

    EndCriteria::Type HybridSimulatedAnnealing<Sampler, Probability, Temperature, Reannealing>::minimize(Problem &P, const EndCriteria &endCriteria) {
        EndCriteria::Type ecType = EndCriteria::None;
        P.reset();
        reannealing_.setProblem(P);
        Array x = P.currentValue();
        Size n = x.size();
        Size k = 1;
        Size kStationary = 1;
        Size kReAnneal = 1;
        Size kReset = 1;
        Size maxK = endCriteria.maxIterations();
        Size maxKStationary = endCriteria.maxStationaryStateIterations();
        bool temperatureBreached = false;
        Array currentTemperature(n, startTemperature_);
        Array annealStep(n, 1.0);
        Array bestPoint(x);
        Array currentPoint(x);
        Array startingPoint(x);
        Array newPoint(x);
        Real bestValue = P.value(bestPoint);
        Real currentValue = bestValue;
        Real startingValue = bestValue; //to reset to starting point if desired
        while (k <= maxK && kStationary <= maxKStationary && !temperatureBreached)
        {
            //Draw a new sample point
            sampler_(newPoint, currentPoint, currentTemperature);

            //Evaluate new point
            Real newValue = P.value(newPoint);

            //Determine if new point is accepted
            if (probability_(currentValue, newValue, currentTemperature)) {
                if (optimizeScheme_ == EveryNewPoint) {
                    P.setCurrentValue(newPoint);
                    P.setFunctionValue(newValue);
                    localOptimizer_->minimize(P, endCriteria);
                    newPoint = P.currentValue();
                    newValue = P.functionValue();
                }
                currentPoint = newPoint;
                currentValue = newValue;
            }

            //Check if we have a new best point
            if (newValue < bestValue) {
                if (optimizeScheme_ == EveryBestPoint) {
                    P.setCurrentValue(newPoint);
                    P.setFunctionValue(newValue);
                    localOptimizer_->minimize(P, endCriteria);
                    newPoint = P.currentValue();
                    newValue = P.functionValue();
                }
                kStationary = 0;
                bestValue = newValue;
                bestPoint = newPoint;
            }

            //Increase steps
            k++;
            kStationary++;
            for (Size i = 0; i < annealStep.size(); i++)
                annealStep[i]++;

            //Reanneal if necessary
            if (kReAnneal == reAnnealSteps_) {
                kReAnneal = 0;
                reannealing_(annealStep, currentPoint, currentValue, currentTemperature);
            }
            kReAnneal++;

            //Reset if necessary
            if (kReset == resetSteps_) {
                kReset = 0;
                switch (resetScheme_) {
                case NoResetScheme:
                    break;
                case ResetToBestPoint:
                    currentPoint = startingPoint;
                    currentValue = startingValue;
                    break;
                case ResetToOrigin:
                    currentPoint = bestPoint;
                    currentValue = bestValue;
                    break;
                }
            }
            kReset++;

            //Update the current temperature according to current step
            temperature_(currentTemperature, currentTemperature, annealStep);

            //Check if temperature condition is breached
            for (Size i = 0; i < n; i++)
                temperatureBreached = temperatureBreached && currentTemperature[i] < endTemperature_;
        }
        //Change end criteria type if appropriate
        if (k > maxK)
            ecType = EndCriteria::MaxIterations;
        else if (kStationary > maxKStationary)
            ecType = EndCriteria::StationaryPoint;
//.........这里部分代码省略.........

    EndCriteria::Type Simplex::minimize(Problem& P,
                                        const EndCriteria& endCriteria) {
        // set up of the problem
        //Real ftol = endCriteria.functionEpsilon();    // end criteria on f(x) (see Numerical Recipes in C++, p.410)
        Real xtol = endCriteria.rootEpsilon();          // end criteria on x (see GSL v. 1.9, http://www.gnu.org/software/gsl/)
        Size maxStationaryStateIterations_
            = endCriteria.maxStationaryStateIterations();
        EndCriteria::Type ecType = EndCriteria::None;
        P.reset();
        Array x_ = P.currentValue();
        Integer iterationNumber_=0;

        // Initialize vertices of the simplex
        bool end = false;
        Size n = x_.size(), i;
        vertices_ = std::vector<Array>(n+1, x_);
        for (i=0; i<n; i++) {
            Array direction(n, 0.0);
            direction[i] = 1.0;
            P.constraint().update(vertices_[i+1], direction, lambda_);
        }
        // Initialize function values at the vertices of the simplex
        values_ = Array(n+1, 0.0);
        for (i=0; i<=n; i++)
            values_[i] = P.value(vertices_[i]);
        // Loop looking for minimum
        do {
            sum_ = Array(n, 0.0);
            Size i;
            for (i=0; i<=n; i++)
                sum_ += vertices_[i];
            // Determine the best (iLowest), worst (iHighest)
            // and 2nd worst (iNextHighest) vertices
            Size iLowest = 0;
            Size iHighest, iNextHighest;
            if (values_[0]<values_[1]) {
                iHighest = 1;
                iNextHighest = 0;
            } else {
                iHighest = 0;
                iNextHighest = 1;
            }
            for (i=1;i<=n; i++) {
                if (values_[i]>values_[iHighest]) {
                    iNextHighest = iHighest;
                    iHighest = i;
                } else {
                    if ((values_[i]>values_[iNextHighest]) && i!=iHighest)
                        iNextHighest = i;
                }
                if (values_[i]<values_[iLowest])
                    iLowest = i;
            }
            // Now compute accuracy, update iteration number and check end criteria
            //// Numerical Recipes exit strategy on fx (see NR in C++, p.410)
            //Real low = values_[iLowest];
            //Real high = values_[iHighest];
            //Real rtol = 2.0*std::fabs(high - low)/
            //    (std::fabs(high) + std::fabs(low) + QL_EPSILON);
            //++iterationNumber_;
            //if (rtol < ftol ||
            //    endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
            // GSL exit strategy on x (see GSL v. 1.9, http://www.gnu.org/software/gsl
            Real simplexSize = computeSimplexSize(vertices_);
            ++iterationNumber_;
            if (simplexSize < xtol ||
                endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
                endCriteria.checkStationaryPoint(0.0, 0.0,
                maxStationaryStateIterations_, ecType);   // PC this is probably not meant like this ? Use separate counter ?
                endCriteria.checkMaxIterations(iterationNumber_, ecType);
                x_ = vertices_[iLowest];
                Real low = values_[iLowest];
                P.setFunctionValue(low);
                P.setCurrentValue(x_);
                return ecType;
            }
            // If end criteria is not met, continue
            Real factor = -1.0;
            Real vTry = extrapolate(P, iHighest, factor);
            if ((vTry <= values_[iLowest]) && (factor == -1.0)) {
                factor = 2.0;
                extrapolate(P, iHighest, factor);
            } else if (std::fabs(factor) > QL_EPSILON) {
                if (vTry >= values_[iNextHighest]) {
                    Real vSave = values_[iHighest];
                    factor = 0.5;
                    vTry = extrapolate(P, iHighest, factor);
                    if (vTry >= vSave && std::fabs(factor) > QL_EPSILON) {
                        for (Size i=0; i<=n; i++) {
                            if (i!=iLowest) {
                                #if defined(QL_ARRAY_EXPRESSIONS)
                                vertices_[i] =
                                    0.5*(vertices_[i] + vertices_[iLowest]);
                                #else
                                vertices_[i] += vertices_[iLowest];
                                vertices_[i] *= 0.5;
                                #endif
                                values_[i] = P.value(vertices_[i]);
                            }
                        }
//.........这里部分代码省略.........

    EndCriteria::Type DifferentialEvolution::minimize(Problem& P,
											const EndCriteria& endCriteria) {

		EndCriteria::Type ecType = EndCriteria::MaxIterations;
	    QL_REQUIRE(P.currentValue().size() == nParam_,
			"Number of parameters mismatch between problem and DE optimizer");
        P.reset();		
		init();

		Real bestCost = QL_MAX_REAL;
		Size bestPop  = 0;
		for (Size p = 0; p < nPop_; ++p) {
			Array tmp(currGen_[p].pop_);
			try {
				currGen_[p].cost_ = P.costFunction().value(tmp);
			} catch (Error&) {
				currGen_[p].cost_ = QL_MAX_REAL;
			}
			if (currGen_[p].cost_ < bestCost) {
				bestPop = p;
				bestCost = currGen_[p].cost_;
			}
		}

		Size lastChange = 0;
		Size lastParamChange = 0;
		for(Size i=0; i<endCriteria.maxIterations(); ++i) {

			Size newBestPop = bestPop;
			Real newBestCost = bestCost;

			for (Size p=0; p<nPop_; ++p) {
				// Find 3 different populations randomly
				Size r1;
				do {
					r1 = static_cast <Size> (uniformRng_.nextInt32() % nPop_);
				}		
				while(r1 == p || r1 == bestPop);

				Size r2;
				do {
					r2 = static_cast <Size> (uniformRng_.nextInt32() % nPop_);
				}
				while ( r2 == p || r2 == bestPop || r2 == r1);

				Size r3;
				do {
					r3 = static_cast <Size> (uniformRng_.nextInt32() % nPop_);
				} while ( r3 == p || r3 == bestPop || r3 == r1 || r3 == r2);

				for(Size j=0; j<nParam_; ++j) {
					nextGen_[p].pop_[j] = currGen_[p].pop_[j];
				}

				Size j = static_cast <Size> (uniformRng_.nextInt32() % nParam_);
				Size L = 0;
				do {
					const double tmp = 
						currGen_[      p].pop_[j] * a0_
					  + currGen_[     r1].pop_[j] * a1_
					  + currGen_[     r2].pop_[j] * a2_
					  + currGen_[     r3].pop_[j] * a3_
					  + currGen_[bestPop].pop_[j] * aBest_;

					nextGen_[p].pop_[j] =
						std::min(maxParams_[j], std::max(minParams_[j], tmp));

					j = (j+1)%nParam_;
					++L;
				} while ((uniformRng_.nextReal() < CR_) && (L < nParam_));

				// Evaluate the new population
				Array tmp(nextGen_[p].pop_);
				try {
					nextGen_[p].cost_ = P.costFunction().value(tmp);
                } catch (Error&) {
					nextGen_[p].cost_ = QL_MAX_REAL;
                }

				// Not better, discard it and keep the old one.
				if (nextGen_[p].cost_ >= currGen_[p].cost_) {
					nextGen_[p] = currGen_[p];
				}
				// Better, keep it.
				else {
					// New best?
					if (nextGen_[p].cost_ < newBestCost) {
						newBestPop = p;
						newBestCost = nextGen_[p].cost_;
					}
				}
			}

			if(std::abs(newBestCost-bestCost) > endCriteria.functionEpsilon()) {
				lastChange = i;
			}
			const Array absDiff = Abs(nextGen_[newBestPop].pop_-currGen_[bestPop].pop_);
			if(*std::max_element(absDiff.begin(), absDiff.end()) > endCriteria.rootEpsilon()) {
				lastParamChange = i;
			}
//.........这里部分代码省略.........

    EndCriteria::Type
    LineSearchBasedMethod::minimize(Problem& P,
                                    const EndCriteria& endCriteria) {
        // Initializations
        Real ftol = endCriteria.functionEpsilon();
        Size maxStationaryStateIterations_
            = endCriteria.maxStationaryStateIterations();
        EndCriteria::Type ecType = EndCriteria::None;   // reset end criteria
        P.reset();                                      // reset problem
        Array x_ = P.currentValue();              // store the starting point
        Size iterationNumber_ = 0;
        // dimension line search
        lineSearch_->searchDirection() = Array(x_.size());
        bool done = false;

        // function and squared norm of gradient values;
        Real fnew, fold, gold2;
        Real fdiff;
        // classical initial value for line-search step
        Real t = 1.0;
        // Set gradient g at the size of the optimization problem
        // search direction
        Size sz = lineSearch_->searchDirection().size();
        Array prevGradient(sz), d(sz), sddiff(sz), direction(sz);
        // Initialize cost function, gradient prevGradient and search direction
        P.setFunctionValue(P.valueAndGradient(prevGradient, x_));
        P.setGradientNormValue(DotProduct(prevGradient, prevGradient));
        lineSearch_->searchDirection() = -prevGradient;

        bool first_time = true;
        // Loop over iterations
        do {
            // Linesearch
            if (!first_time)
                prevGradient = lineSearch_->lastGradient();
            t = (*lineSearch_)(P, ecType, endCriteria, t);
            // don't throw: it can fail just because maxIterations exceeded
            //QL_REQUIRE(lineSearch_->succeed(), "line-search failed!");
            if (lineSearch_->succeed())
            {
                // Updates

                // New point
                x_ = lineSearch_->lastX();
                // New function value
                fold = P.functionValue();
                P.setFunctionValue(lineSearch_->lastFunctionValue());
                // New gradient and search direction vectors

                // orthogonalization coef
                gold2 = P.gradientNormValue();
                P.setGradientNormValue(lineSearch_->lastGradientNorm2());

                // conjugate gradient search direction
                direction = getUpdatedDirection(P, gold2, prevGradient);

                sddiff = direction - lineSearch_->searchDirection();
                lineSearch_->searchDirection() = direction;
                // Now compute accuracy and check end criteria
                // Numerical Recipes exit strategy on fx (see NR in C++, p.423)
                fnew = P.functionValue();
                fdiff = 2.0*std::fabs(fnew-fold) /
                        (std::fabs(fnew) + std::fabs(fold) + QL_EPSILON);
                if (fdiff < ftol ||
                    endCriteria.checkMaxIterations(iterationNumber_, ecType)) {
                    endCriteria.checkStationaryFunctionValue(0.0, 0.0,
                        maxStationaryStateIterations_, ecType);
                    endCriteria.checkMaxIterations(iterationNumber_, ecType);
                    return ecType;
                }
                P.setCurrentValue(x_);      // update problem current value
                ++iterationNumber_;         // Increase iteration number
                first_time = false;
            } else {
                done = true;
            }
        } while (!done);
        P.setCurrentValue(x_);
        return ecType;
    }