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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

 Copyright (C) 2003, 2004 Ferdinando Ametrano

 This file is part of QuantLib, a free-software/open-source library
 for financial quantitative analysts and developers - http://quantlib.org/

 QuantLib is free software: you can redistribute it and/or modify it
 under the terms of the QuantLib license.  You should have received a
 copy of the license along with this program; if not, please email
 <quantlib-dev@lists.sf.net>. The license is also available online at

 This program is distributed in the hope that it will be useful, but WITHOUT
 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 FOR A PARTICULAR PURPOSE.  See the license for more details.

/*! \file pseudosqrt.hpp
    \brief pseudo square root of a real symmetric matrix

#ifndef quantlib_pseudo_sqrt_hpp
#define quantlib_pseudo_sqrt_hpp

#include <ql/Math/matrix.hpp>

namespace QuantLib {

    //! algorithm used for matricial pseudo square root
00032     struct SalvagingAlgorithm {
        enum Type {None, Spectral, Hypersphere};

    //! Returns the pseudo square root of a real symmetric matrix
    /*! Given a matrix \f$ M \f$, the result \f$ S \f$ is defined
        as the matrix such that \f$ S S^T = M. \f$
        If the matrix is not positive semi definite, it can
        return an approximation of the pseudo square root
        using a (user selected) salvaging algorithm.

        For more information see: "The most general methodology to create
        a valid correlation matrix for risk management and option pricing
        purposes", by R. Rebonato and P. Jäckel.
        The Journal of Risk, 2(2), Winter 1999/2000

        Revised and extended in "Monte Carlo Methods in Finance",
        by Peter Jäckel, Chapter 6.

        \pre the given matrix must be symmetric.

        \relates Matrix

        - implement Hypersphere decomposition:
              -# Jäckel "Monte Carlo Methods in Finance", Chapter 6
              -# Brigo "A Note on Correlation and Rank Reduction"
              -# Rapisarda, Brigo, Mercurio "Parameterizing correlations:
                 a geometric interpretation"
        - implement Higham algorithm:
          Higham "Computing the nearest correlation matrix"

        - the correctness of the results is tested by reproducing
          known good data.
        - the correctness of the results is tested by checking
          returned values against numerical calculations.
    const Disposable<Matrix> pseudoSqrt(const Matrix&,

    /*! \pre the given matrix must be symmetric.

        \relates Matrix
    const Disposable<Matrix> rankReducedSqrt(const Matrix&,
                                             Size maxRank,
                                             Real componentRetainedPercentage,



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