// Copyright (C) 2020-2023 Yixuan Qiu <yixuan.qiu@cos.name>
// Under MIT license

#pragma once

#include <vector>
#ifdef R_BUILD
#include <RcppEigen.h>
#else
#include <Eigen/Core>
#endif
#include "BKLDLT.h"

/// \cond

namespace LBFGSpp {

//
// An *implicit* representation of the BFGS approximation to the Hessian matrix B
//
// B = theta * I - W * M * W'
// H = inv(B)
//
// Reference:
// [1] D. C. Liu and J. Nocedal (1989). On the limited memory BFGS method for large scale optimization.
// [2] R. H. Byrd, P. Lu, and J. Nocedal (1995). A limited memory algorithm for bound constrained optimization.
//
template <typename Scalar, bool LBFGSB = false>
class BFGSMat
{
private:
    using Vector = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
    using Matrix = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>;
    using RefConstVec = Eigen::Ref<const Vector>;
    using IndexSet = std::vector<int>;

    int m_m;         // Maximum number of correction vectors
    Scalar m_theta;  // theta * I is the initial approximation to the Hessian matrix
    Matrix m_s;      // History of the s vectors
    Matrix m_y;      // History of the y vectors
    Vector m_ys;     // History of the s'y values
    Vector m_alpha;  // Temporary values used in computing H * v
    int m_ncorr;     // Number of correction vectors in the history, m_ncorr <= m
    int m_ptr;       // A Pointer to locate the most recent history, 1 <= m_ptr <= m
                     // Details: s and y vectors are stored in cyclic order.
                     //          For example, if the current s-vector is stored in m_s[, m-1],
                     //          then in the next iteration m_s[, 0] will be overwritten.
                     //          m_s[, m_ptr-1] points to the most recent history,
                     //          and m_s[, m_ptr % m] points to the most distant one.

    //========== The following members are only used in L-BFGS-B algorithm ==========//
    Matrix m_permMinv;             // Permutated M inverse
    BKLDLT<Scalar> m_permMsolver;  // Represents the permutated M matrix

public:
    // Constructor
    BFGSMat() {}

    // Reset internal variables
    // n: dimension of the vector to be optimized
    // m: maximum number of corrections to approximate the Hessian matrix
    inline void reset(int n, int m)
    {
        m_m = m;
        m_theta = Scalar(1);
        m_s.resize(n, m);
        m_y.resize(n, m);
        m_ys.resize(m);
        m_alpha.resize(m);
        m_ncorr = 0;
        m_ptr = m;  // This makes sure that m_ptr % m == 0 in the first step

        if (LBFGSB)
        {
            m_permMinv.resize(2 * m, 2 * m);
            m_permMinv.setZero();
            m_permMinv.diagonal().setOnes();
        }
    }

    // Add correction vectors to the BFGS matrix
    inline void add_correction(const RefConstVec& s, const RefConstVec& y)
    {
        const int loc = m_ptr % m_m;

        m_s.col(loc).noalias() = s;
        m_y.col(loc).noalias() = y;

        // ys = y's = 1/rho
        const Scalar ys = m_s.col(loc).dot(m_y.col(loc));
        m_ys[loc] = ys;

        m_theta = m_y.col(loc).squaredNorm() / ys;

        if (m_ncorr < m_m)
            m_ncorr++;

        m_ptr = loc + 1;

        if (LBFGSB)
        {
            // Minv = [-D         L']
            //        [ L  theta*S'S]

            // Copy -D
            // Let S=[s[0], ..., s[m-1]], Y=[y[0], ..., y[m-1]]
            // D = [s[0]'y[0], ..., s[m-1]'y[m-1]]
            m_permMinv(loc, loc) = -ys;

            // Update S'S
            // We only store S'S in Minv, and multiply theta when LU decomposition is performed
            Vector Ss = m_s.leftCols(m_ncorr).transpose() * m_s.col(loc);
            m_permMinv.block(m_m + loc, m_m, 1, m_ncorr).noalias() = Ss.transpose();
            m_permMinv.block(m_m, m_m + loc, m_ncorr, 1).noalias() = Ss;

            // Compute L
            // L = [          0                                     ]
            //     [  s[1]'y[0]             0                       ]
            //     [  s[2]'y[0]     s[2]'y[1]                       ]
            //     ...
            //     [s[m-1]'y[0] ... ... ... ... ... s[m-1]'y[m-2]  0]
            //
            // L_next = [        0                                   ]
            //          [s[2]'y[1]             0                     ]
            //          [s[3]'y[1]     s[3]'y[2]                     ]
            //          ...
            //          [s[m]'y[1] ... ... ... ... ... s[m]'y[m-1]  0]
            const int len = m_ncorr - 1;
            // First zero out the column of oldest y
            if (m_ncorr >= m_m)
                m_permMinv.block(m_m, loc, m_m, 1).setZero();
            // Compute the row associated with new s
            // The current row is loc
            // End with column (loc + m - 1) % m
            // Length is len
            int yloc = (loc + m_m - 1) % m_m;
            for (int i = 0; i < len; i++)
            {
                m_permMinv(m_m + loc, yloc) = m_s.col(loc).dot(m_y.col(yloc));
                yloc = (yloc + m_m - 1) % m_m;
            }

            // Matrix LDLT factorization
            m_permMinv.block(m_m, m_m, m_m, m_m) *= m_theta;
            m_permMsolver.compute(m_permMinv);
            m_permMinv.block(m_m, m_m, m_m, m_m) /= m_theta;
        }
    }

    // Recursive formula to compute a * H * v, where a is a scalar, and v is [n x 1]
    // H0 = (1/theta) * I is the initial approximation to H
    // Algorithm 7.4 of Nocedal, J., & Wright, S. (2006). Numerical optimization.
    inline void apply_Hv(const Vector& v, const Scalar& a, Vector& res)
    {
        res.resize(v.size());

        // L-BFGS two-loop recursion

        // Loop 1
        res.noalias() = a * v;
        int j = m_ptr % m_m;
        for (int i = 0; i < m_ncorr; i++)
        {
            j = (j + m_m - 1) % m_m;
            m_alpha[j] = m_s.col(j).dot(res) / m_ys[j];
            res.noalias() -= m_alpha[j] * m_y.col(j);
        }

        // Apply initial H0
        res /= m_theta;

        // Loop 2
        for (int i = 0; i < m_ncorr; i++)
        {
            const Scalar beta = m_y.col(j).dot(res) / m_ys[j];
            res.noalias() += (m_alpha[j] - beta) * m_s.col(j);
            j = (j + 1) % m_m;
        }
    }

    //========== The following functions are only used in L-BFGS-B algorithm ==========//

    // Return the value of theta
    inline Scalar theta() const { return m_theta; }

    // Return current number of correction vectors
    inline int num_corrections() const { return m_ncorr; }

    // W = [Y, theta * S]
    // W [n x (2*ncorr)], v [n x 1], res [(2*ncorr) x 1]
    // res preserves the ordering of Y and S columns
    inline void apply_Wtv(const Vector& v, Vector& res) const
    {
        res.resize(2 * m_ncorr);
        res.head(m_ncorr).noalias() = m_y.leftCols(m_ncorr).transpose() * v;
        res.tail(m_ncorr).noalias() = m_theta * m_s.leftCols(m_ncorr).transpose() * v;
    }

    // The b-th row of the W matrix
    // Preserves the ordering of Y and S columns
    // Return as a column vector
    inline Vector Wb(int b) const
    {
        Vector res(2 * m_ncorr);
        for (int j = 0; j < m_ncorr; j++)
        {
            res[j] = m_y(b, j);
            res[m_ncorr + j] = m_s(b, j);
        }
        res.tail(m_ncorr) *= m_theta;
        return res;
    }

    // Extract rows of W
    inline Matrix Wb(const IndexSet& b) const
    {
        const int nb = b.size();
        const int* bptr = b.data();
        Matrix res(nb, 2 * m_ncorr);

        for (int j = 0; j < m_ncorr; j++)
        {
            const Scalar* Yptr = &m_y(0, j);
            const Scalar* Sptr = &m_s(0, j);
            Scalar* resYptr = res.data() + j * nb;
            Scalar* resSptr = resYptr + m_ncorr * nb;
            for (int i = 0; i < nb; i++)
            {
                const int row = bptr[i];
                resYptr[i] = Yptr[row];
                resSptr[i] = Sptr[row];
            }
        }
        return res;
    }

    // M is [(2*ncorr) x (2*ncorr)], v is [(2*ncorr) x 1]
    inline void apply_Mv(const Vector& v, Vector& res) const
    {
        res.resize(2 * m_ncorr);
        if (m_ncorr < 1)
            return;

        Vector vpadding = Vector::Zero(2 * m_m);
        vpadding.head(m_ncorr).noalias() = v.head(m_ncorr);
        vpadding.segment(m_m, m_ncorr).noalias() = v.tail(m_ncorr);

        // Solve linear equation
        m_permMsolver.solve_inplace(vpadding);

        res.head(m_ncorr).noalias() = vpadding.head(m_ncorr);
        res.tail(m_ncorr).noalias() = vpadding.segment(m_m, m_ncorr);
    }

    // Compute W'Pv
    // W [n x (2*ncorr)], v [nP x 1], res [(2*ncorr) x 1]
    // res preserves the ordering of Y and S columns
    // Returns false if the result is known to be zero
    inline bool apply_WtPv(const IndexSet& P_set, const Vector& v, Vector& res, bool test_zero = false) const
    {
        const int* Pptr = P_set.data();
        const Scalar* vptr = v.data();
        int nP = P_set.size();

        // Remove zeros in v to save computation
        IndexSet P_reduced;
        std::vector<Scalar> v_reduced;
        if (test_zero)
        {
            P_reduced.reserve(nP);
            for (int i = 0; i < nP; i++)
            {
                if (vptr[i] != Scalar(0))
                {
                    P_reduced.push_back(Pptr[i]);
                    v_reduced.push_back(vptr[i]);
                }
            }
            Pptr = P_reduced.data();
            vptr = v_reduced.data();
            nP = P_reduced.size();
        }

        res.resize(2 * m_ncorr);
        if (m_ncorr < 1 || nP < 1)
        {
            res.setZero();
            return false;
        }

        for (int j = 0; j < m_ncorr; j++)
        {
            Scalar resy = Scalar(0), ress = Scalar(0);
            const Scalar* yptr = &m_y(0, j);
            const Scalar* sptr = &m_s(0, j);
            for (int i = 0; i < nP; i++)
            {
                const int row = Pptr[i];
                resy += yptr[row] * vptr[i];
                ress += sptr[row] * vptr[i];
            }
            res[j] = resy;
            res[m_ncorr + j] = ress;
        }
        res.tail(m_ncorr) *= m_theta;
        return true;
    }

    // Compute s * P'WMv
    // Assume that v[2*ncorr x 1] has the same ordering (permutation) as W and M
    // Returns false if the result is known to be zero
    inline bool apply_PtWMv(const IndexSet& P_set, const Vector& v, Vector& res, const Scalar& scale) const
    {
        const int nP = P_set.size();
        res.resize(nP);
        res.setZero();
        if (m_ncorr < 1 || nP < 1)
            return false;

        Vector Mv;
        apply_Mv(v, Mv);
        // WP * Mv
        Mv.tail(m_ncorr) *= m_theta;
        for (int j = 0; j < m_ncorr; j++)
        {
            const Scalar* yptr = &m_y(0, j);
            const Scalar* sptr = &m_s(0, j);
            const Scalar Mvy = Mv[j], Mvs = Mv[m_ncorr + j];
            for (int i = 0; i < nP; i++)
            {
                const int row = P_set[i];
                res[i] += Mvy * yptr[row] + Mvs * sptr[row];
            }
        }
        res *= scale;
        return true;
    }
    // If the P'W matrix has been explicitly formed, do a direct matrix multiplication
    inline bool apply_PtWMv(const Matrix& WP, const Vector& v, Vector& res, const Scalar& scale) const
    {
        const int nP = WP.rows();
        res.resize(nP);
        if (m_ncorr < 1 || nP < 1)
        {
            res.setZero();
            return false;
        }

        Vector Mv;
        apply_Mv(v, Mv);
        // WP * Mv
        Mv.tail(m_ncorr) *= m_theta;
        res.noalias() = scale * (WP * Mv);
        return true;
    }

    // Compute F'BAb = -(F'W)M(W'AA'd)
    // W'd is known, and AA'+FF'=I, so W'AA'd = W'd - W'FF'd
    // Usually d contains many zeros, so we fist compute number of nonzero elements in A set and F set,
    // denoted as nnz_act and nnz_fv, respectively
    // If nnz_act is smaller, compute W'AA'd = WA' (A'd) directly
    // If nnz_fv is smaller, compute W'AA'd = W'd - WF' * (F'd)
    inline void compute_FtBAb(
        const Matrix& WF, const IndexSet& fv_set, const IndexSet& newact_set, const Vector& Wd, const Vector& drt,
        Vector& res) const
    {
        const int nact = newact_set.size();
        const int nfree = WF.rows();
        res.resize(nfree);
        if (m_ncorr < 1 || nact < 1 || nfree < 1)
        {
            res.setZero();
            return;
        }

        // W'AA'd
        Vector rhs(2 * m_ncorr);
        if (nact <= nfree)
        {
            // Construct A'd
            Vector Ad(nfree);
            for (int i = 0; i < nact; i++)
                Ad[i] = drt[newact_set[i]];
            apply_WtPv(newact_set, Ad, rhs);
        }
        else
        {
            // Construct F'd
            Vector Fd(nfree);
            for (int i = 0; i < nfree; i++)
                Fd[i] = drt[fv_set[i]];
            // Compute W'AA'd = W'd - WF' * (F'd)
            rhs.noalias() = WF.transpose() * Fd;
            rhs.tail(m_ncorr) *= m_theta;
            rhs.noalias() = Wd - rhs;
        }

        apply_PtWMv(WF, rhs, res, Scalar(-1));
    }

    // Compute inv(P'BP) * v
    // P represents an index set
    // inv(P'BP) * v = v / theta + WP * inv(inv(M) - WP' * WP / theta) * WP' * v / theta^2
    //
    // v is [nP x 1]
    inline void solve_PtBP(const Matrix& WP, const Vector& v, Vector& res) const
    {
        const int nP = WP.rows();
        res.resize(nP);
        if (m_ncorr < 1 || nP < 1)
        {
            res.noalias() = v / m_theta;
            return;
        }

        // Compute the matrix in the middle (only the lower triangular part is needed)
        // Remember that W = [Y, theta * S], but we do not store theta in WP
        Matrix mid(2 * m_ncorr, 2 * m_ncorr);
        // [0:(ncorr - 1), 0:(ncorr - 1)]
        for (int j = 0; j < m_ncorr; j++)
        {
            mid.col(j).segment(j, m_ncorr - j).noalias() = m_permMinv.col(j).segment(j, m_ncorr - j) -
                WP.block(0, j, nP, m_ncorr - j).transpose() * WP.col(j) / m_theta;
        }
        // [ncorr:(2 * ncorr - 1), 0:(ncorr - 1)]
        mid.block(m_ncorr, 0, m_ncorr, m_ncorr).noalias() = m_permMinv.block(m_m, 0, m_ncorr, m_ncorr) -
            WP.rightCols(m_ncorr).transpose() * WP.leftCols(m_ncorr);
        // [ncorr:(2 * ncorr - 1), ncorr:(2 * ncorr - 1)]
        for (int j = 0; j < m_ncorr; j++)
        {
            mid.col(m_ncorr + j).segment(m_ncorr + j, m_ncorr - j).noalias() = m_theta *
                (m_permMinv.col(m_m + j).segment(m_m + j, m_ncorr - j) - WP.rightCols(m_ncorr - j).transpose() * WP.col(m_ncorr + j));
        }
        // Factorization
        BKLDLT<Scalar> midsolver(mid);
        // Compute the final result
        Vector WPv = WP.transpose() * v;
        WPv.tail(m_ncorr) *= m_theta;
        midsolver.solve_inplace(WPv);
        WPv.tail(m_ncorr) *= m_theta;
        res.noalias() = v / m_theta + (WP * WPv) / (m_theta * m_theta);
    }

    // Compute P'BQv, where P and Q are two mutually exclusive index selection operators
    // P'BQv = -WP * M * WQ' * v
    // Returns false if the result is known to be zero
    inline bool apply_PtBQv(const Matrix& WP, const IndexSet& Q_set, const Vector& v, Vector& res, bool test_zero = false) const
    {
        const int nP = WP.rows();
        const int nQ = Q_set.size();
        res.resize(nP);
        if (m_ncorr < 1 || nP < 1 || nQ < 1)
        {
            res.setZero();
            return false;
        }

        Vector WQtv;
        bool nonzero = apply_WtPv(Q_set, v, WQtv, test_zero);
        if (!nonzero)
        {
            res.setZero();
            return false;
        }

        Vector MWQtv;
        apply_Mv(WQtv, MWQtv);
        MWQtv.tail(m_ncorr) *= m_theta;
        res.noalias() = -WP * MWQtv;
        return true;
    }
    // If the Q'W matrix has been explicitly formed, do a direct matrix multiplication
    inline bool apply_PtBQv(const Matrix& WP, const Matrix& WQ, const Vector& v, Vector& res) const
    {
        const int nP = WP.rows();
        const int nQ = WQ.rows();
        res.resize(nP);
        if (m_ncorr < 1 || nP < 1 || nQ < 1)
        {
            res.setZero();
            return false;
        }

        // Remember that W = [Y, theta * S], so we need to multiply theta to the second half
        Vector WQtv = WQ.transpose() * v;
        WQtv.tail(m_ncorr) *= m_theta;
        Vector MWQtv;
        apply_Mv(WQtv, MWQtv);
        MWQtv.tail(m_ncorr) *= m_theta;
        res.noalias() = -WP * MWQtv;
        return true;
    }
};

}  // namespace LBFGSpp

/// \endcond
