function [dr,info] = dyn_risky_steadystate_solver(ys0,M, ...
                                                  dr,options,oo)

    %@info:
    %! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
    %! @anchor{dyn_risky_steadystate_solver}
    %! @sp 1
    %! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
    %! @sp 2
    %! @strong{Inputs}
    %! @sp 1
    %! @table @ @var
    %! @item ys0
    %! Vector containing a guess value for the risky steady state
    %! @item M
    %! Matlab's structure describing the model (initialized by @code{dynare}).
    %! @item dr
    %! Matlab's structure describing the reduced form solution of the model.
    %! @item options
    %! Matlab's structure describing the options (initialized by @code{dynare}).
    %! @item oo
    %! Matlab's structure gathering the results (initialized by @code{dynare}).
    %! @end table
    %! @sp 2
    %! @strong{Outputs}
    %! @sp 1
    %! @table @ @var
    %! @item dr
    %! Matlab's structure describing the reduced form solution of the model.
    %! @item info
    %! Integer scalar, error code.
    %! @sp 1
    %! @table @ @code
    %! @item info==0
    %! No error.
    %! @item info==1
    %! The model doesn't determine the current variables uniquely.
    %! @item info==2
    %! MJDGGES returned an error code.
    %! @item info==3
    %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
    %! @item info==4
    %! Blanchard & Kahn conditions are not satisfied: indeterminacy.
    %! @item info==5
    %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
    %! @item info==6
    %! The jacobian evaluated at the deterministic steady state is complex.
    %! @item info==19
    %! The steadystate routine thrown an exception (inconsistent deep parameters).
    %! @item info==20
    %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
    %! @item info==21
    %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
    %! @item info==22
    %! The steady has NaNs.
    %! @item info==23
    %! M_.params has been updated in the steadystate routine and has complex valued scalars.
    %! @item info==24
    %! M_.params has been updated in the steadystate routine and has some NaNs.
    %! @end table
    %! @end table
    %! @end deftypefn
    %@eod:

    % Copyright (C) 2001-2012 Dynare Team
    %
    % This file is part of Dynare.
    %
    % Dynare is free software: you can redistribute it and/or modify
    % it under the terms of the GNU General Public License as published by
    % the Free Software Foundation, either version 3 of the License, or
    % (at your option) any later version.
    %
    % Dynare 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
    % GNU General Public License for more details.
    %
    % You should have received a copy of the GNU General Public License
    % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.

    
    info = 0;
    lead_lag_incidence = M.lead_lag_incidence;
    order_var = dr.order_var;
    endo_nbr = M.endo_nbr;
    exo_nbr = M.exo_nbr;
    
    M.var_order_endo_names = M.endo_names(dr.order_var,:);
    
    [junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
    dr.i_fwrd_f = i_fwrd_f;
    nd = nnz(lead_lag_incidence) + M.exo_nbr;
    dr.nd = nd;
    kk = reshape(1:nd^2,nd,nd);
    kkk = reshape(1:nd^3,nd^2,nd);
    dr.i_fwrd2_f = kk(i_fwrd_f,i_fwrd_f);
    dr.i_fwrd2a_f = kk(i_fwrd_f,:);
    dr.i_fwrd3_f = kkk(dr.i_fwrd2_f,:);
    dr.i_uu = kk(end-exo_nbr+1:end,end-exo_nbr+1:end);
    if options.k_order_solver
        func = @risky_residuals_k_order;
    else
        func = @risky_residuals;
    end
    
    if isfield(options,'portfolio') && options.portfolio == 1
        pm = portfolio_model_structure(M,options);
        
        x0 = ys0(pm.v_p);
        n = length(x0);
        [x, info] = solve1(@risky_residuals_ds,x0,1:n,1:n,0,1, options.gstep, ...
                           options.solve_tolf,options.solve_tolx, ...
                           options.steady.maxit,options.debug,pm,M,dr, ...
                           options,oo);
        if info
            error('DS approach can''t be computed')
        end
        %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
        %        ys0(l_var) = x;
        [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo); 
        ys1 = dr1.ys;
    else
        pm = model_structure(M,options);
    end
    
    [ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,1, options.gstep, ...
                        options.solve_tolf,options.solve_tolx, ...
                        options.steady.maxit,options.debug,pm,M,dr,options,oo);
    %    [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
    if info
        error('RSS approach can''t be computed')
    end
    dr.ys = ys;

    [resid,dr] = func(ys,pm,M,dr,options,oo);
    dr.ghs2 = zeros(M.endo_nbr,1);
    
    for i=1:M.endo_nbr
        if isfield(options,'portfolio') && options.portfolio == 1
            disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys1(i), ...
                         ys(i)))
        else
            disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys(i)))
        end
    end

end

function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
    
    lead_lag_incidence = M.lead_lag_incidence;
    iyv = lead_lag_incidence';
    iyv = iyv(:);
    iyr0 = find(iyv) ;
    
    if M.exo_nbr == 0
        oo.exo_steady_state = [] ;
    end
    
    z = repmat(ys,1,3);
    z = z(iyr0) ;
    [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
                           [oo.exo_simul ...
                        oo.exo_det_simul], M.params, dr.ys, 2);
    if ~isreal(d1) || ~isreal(d2)
        pause
    end
    
    if options.use_dll
        % In USE_DLL mode, the hessian is in the 3-column sparse representation
        d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
                    size(d1, 1), size(d1, 2)*size(d1, 2));
    end

    if isfield(options,'portfolio') && options.portfolio == 1
        pm = portfolio_model_structure(M,options);
        x = ys(pm.v_p);
        dr = first_step_ds(x,pm,M,dr,options,oo);
        dr.ys = ys;
    else
        pm = model_structure(M,options);
        [dr,info] = dyn_first_order_solver(d1,M,dr,options,0);
        if info
            print_info(info,options.noprint,options);
        end
        dr = dyn_second_order_solver(d1,d2,dr,M,...
                                     options.threads.kronecker.A_times_B_kronecker_C,...
                                     options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
    end
    
    gu1 = dr.ghu(pm.i_fwrd_g,:);

    resid = resid1+0.5*(d1(:,pm.i_fwrd_f1)*dr.ghuu(pm.i_fwrd_g,:)+ ...
                        d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
end

function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
    
    v_p = pm.v_p;
    v_np = pm.v_np;
    
    % computing steady state of non-portfolio variables  consistent with
    % assumed portfolio 
    dr.ys(v_p) = x;
    ys0 = dr.ys(v_np);
    f_h =str2func([M.fname '_static']);
    [dr.ys(v_np),info] = csolve(@ds_static_model,ys0,[],1e-10,100,f_h,x,pm.eq_np,v_np,v_p, ...
                                M.endo_nbr,M.exo_nbr,M.params);
    if info
        error('can''t compute non-portfolio steady state')
    end
    
    dr_np = first_step_ds(x,pm,M,dr,options,oo);

    lead_lag_incidence = M.lead_lag_incidence;
    iyv = lead_lag_incidence';
    iyv = iyv(:);
    iyr0 = find(iyv) ;
    
    z = repmat(dr.ys,1,3);
    z = z(iyr0) ;
    [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
                           [oo.exo_simul ...
                        oo.exo_det_simul], M.params, dr.ys, 2);
    if ~isreal(d1) || ~isreal(d2)
        pause
    end
    
    if options.use_dll
        % In USE_DLL mode, the hessian is in the 3-column sparse representation
        d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
                    size(d1, 1), size(d1, 2)*size(d1, 2));
    end

    
    gu1 = dr_np.ghu(pm.i_fwrd_g,:);

    resid = resid1+0.5*(d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);

    resid = resid(pm.eq_p)
end

function dr_np = first_step_ds(x,pm,M,dr,options,oo)

    lead_lag_incidence = M.lead_lag_incidence;
    iyv = lead_lag_incidence';
    iyv = iyv(:);
    iyr0 = find(iyv) ;

    ys = dr.ys;
    ys(pm.v_p) = x;
    
    z = repmat(ys,1,3);
    z = z(iyr0) ;
    [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
                           [oo.exo_simul ...
                        oo.exo_det_simul], M.params, dr.ys, 2);
    if ~isreal(d1) || ~isreal(d2)
        pause
    end
    
    if options.use_dll
        % In USE_DLL mode, the hessian is in the 3-column sparse representation
        d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
                    size(d1, 1), size(d1, 2)*size(d1, 2));
    end

    d1_np = d1(pm.eq_np,pm.i_d1_np);
    d2_np = d2(pm.eq_np,pm.i_d2_np);
    
    [dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
    if info
        print_info(info, 0, options);
        return
    end
    
    dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
                                    options.threads.kronecker.A_times_B_kronecker_C,...
                                    options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
end

function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
    exo_nbr = M.exo_nbr;
    endo_nbr = M.endo_nbr;
    
    iyv = M.lead_lag_incidence';
    iyv = iyv(:);
    iyr0 = find(iyv) ;
    
    if exo_nbr == 0
        oo.exo_steady_state = [] ;
    end
    
    z = repmat(ys,1,3);
    z = z(iyr0) ;
    [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
                              [oo.exo_simul ...
                        oo.exo_det_simul], M.params, dr.ys, 2);
    
    if isfield(options,'portfolio') && options.portfolio == 1
        eq_np = pm.eq_np;
        
        d1_np = d1(eq_np,pm.i_d1_np);
        d2_np = d2(eq_np,pm.i_d2_np);

        M_np = pm.M_np;
        dr_np = pm.dr_np;
        
        [dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
        if info
            print_info(info, 0, options);
            return
        end
        
        dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
                                        options.threads.kronecker.A_times_B_kronecker_C,...
                                        options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
    end
    
    i_fwrd_f1 = pm.i_fwrd_f1;
    i_fwrd_f2 = pm.i_fwrd_f2;
    i_fwrd_f3 = pm.i_fwrd_f3;
    i_fwrd_g = pm.i_fwrd_g;
    gu1 = dr_np.ghu(i_fwrd_g,:);
    ghuu = dr_np.ghuu;
    
    resid = resid1+0.5*(d1(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
                        kron(gu1,gu1))*vec(M.Sigma_e);

    if nargout > 1
        [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
                                  [oo.exo_simul ...
                            oo.exo_det_simul], M.params, dr.ys, 2);

        
        [a,b,c] = find(d2(eq_np,pm.i_d2_np));
        d2_np = [a b c];
        
        [a,b,c] = find(d3(eq_np,pm.i_d3_np));
        d3_np = [a b c];
         
        options.order = 3;
        % space holder, unused by k_order_pertrubation
        dr_np.ys = dr.ys(pm.v_np);
        nu2 = exo_nbr*(exo_nbr+1)/2;
        nu3 = exo_nbr*(exo_nbr+1)*(exo_nbr+2)/3;
        M_np.NZZDerivatives = [nnz(d1_np); nnz(d2_np); nnz(d3_np)];
        [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
        mexErrCheck('k_order_perturbation', err);

        gu1 = g_1(i_fwrd_g,end-exo_nbr+1:end);
        ghuu = unfold2(g_2(:,end-nu2+1:end),exo_nbr);
        ghsuu = get_ghsuu(g_3,size(g_1,2),exo_nbr);

        i_fwrd1_f2 = pm.i_fwrd1_f2;
        i_fwrd1_f3 = pm.i_fwrd1_f3;
        n = size(d1,2);
        d1b = d1 + 0.5*( ...
            d1(:,i_fwrd_f1)*...
            d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e))...
            + 0.5*d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e)));
        format short
        kk1 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
               nnz(M.lead_lag_incidence)+[1; 2]]
        kk2 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
               nnz(M.lead_lag_incidence)+[3; 4]]
        format short
        gu1
        kron(gu1,gu1)*vec(M.Sigma_e)
        disp(d1(:,:))
        disp(d1b(:,:))
        aa2=d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e));
        aa3=d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e));
        disp(d3(4,7+6*n+6*n*n))
        disp(d3(4,8+16*n+17*n*n))   %8,17,18
        disp(d3(4,8+17*n+16*n*n))   %8,17,18
        disp(d3(4,7*n+17+17*n*n))   %8,17,18
        disp(d3(4,7*n+18+16*n*n))   %8,17,18
        disp(d3(4,7*n*n+16*n+18))   %8,17,18
        disp(d3(4,7*n*n+17+17*n))   %8,17,18
        pause
        disp(aa2(:,kk1))
        disp(aa2(:,kk2))
        disp(aa3(:,kk1))
        disp(aa3(:,kk2))
        [dr,info] = dyn_first_order_solver(d1b,M,dr,options,0);
        if info
            print_info(info, 0, options);
            return
        end
        
        disp_dr(dr,dr.order_var,[]);
        
    end
end

function y=unfold2(x,n)
    y = zeros(size(x,1),n*n);
    k = 1;
    for i=1:n
        for j=i:n
            y(:,(i-1)*n+j) = x(:,k);
            if i ~= j
                y(:,(j-1)*n+i) = x(:,k);
            end
            k = k+1;
        end
    end
end

function y=unfold3(x,n)
    y = zeros(size(x,1),n*n*n);
    k = 1;
    for i=1:n
        for j=i:n
            for m=j:n
                y(:,(i-1)*n*n+(j-1)*n+m) = x(:,k);
                y(:,(i-1)*n*n+(m-1)*n+j) = x(:,k);
                y(:,(j-1)*n*n+(i-1)*n+m) = x(:,k);
                y(:,(j-1)*n*n+(m-1)*n+i) = x(:,k);
                y(:,(m-1)*n*n+(i-1)*n+j) = x(:,k);
                y(:,(m-1)*n*n+(j-1)*n+i) = x(:,k);
                
                k = k+1;
            end
        end
    end
end

function pm  = model_structure(M,options)


    lead_index = M.maximum_endo_lag+2;
    lead_lag_incidence = M.lead_lag_incidence;
    dr = struct();
    dr = set_state_space(dr,M,options);
    pm.i_fwrd_g = find(lead_lag_incidence(lead_index,dr.order_var)');    

    i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,dr.order_var));
    pm.i_fwrd_f1 = i_fwrd_f1;
    n = nnz(lead_lag_incidence)+M.exo_nbr;
    ih = reshape(1:n*n,n,n);
    i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
    pm.i_fwrd_f2 = i_fwrd_f2(:);
    i_fwrd1_f2 = ih(i_fwrd_f1,:);
    pm.i_fwrd1_f2 = i_fwrd1_f2(:);

    ih = reshape(1:n*n*n,n,n,n);
    i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
    pm.i_fwrd_f3 = i_fwrd_f3(:);
    i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
    pm.i_fwrd1_f3 = i_fwrd1_f3(:);
end

function pm  = portfolio_model_structure(M,options)

    i_d3_np = [];
    i_d3_p = [];

    lead_index = M.maximum_endo_lag+2;
    lead_lag_incidence = M.lead_lag_incidence;
    eq_tags = M.equations_tags;
    n_tags = size(eq_tags,1);
    eq_p = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
                                   'portfolio'),1));
    pm.eq_p = eq_p;
    pm.eq_np = setdiff(1:M.endo_nbr,eq_p);
    v_p = zeros(n_tags,1);
    for i=1:n_tags
        v_p(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
                              length(cell2mat(eq_tags(i,3)))));
    end
    if any(lead_lag_incidence(lead_index,v_p))
        error(['portfolio variables appear in the model as forward ' ...
               'variable'])
    end
    pm.v_p = v_p;
    v_np = setdiff(1:M.endo_nbr,v_p);
    pm.v_np = v_np;
    lli_np = lead_lag_incidence(:,v_np)';
    k = find(lli_np);
    lead_lag_incidence_np = lli_np;
    lead_lag_incidence_np(k) = 1:nnz(lli_np);
    lead_lag_incidence_np = lead_lag_incidence_np';
    pm.lead_lag_incidence_np = lead_lag_incidence_np;
    i_d1_np = [nonzeros(lli_np); nnz(lead_lag_incidence)+(1:M.exo_nbr)'];
    pm.i_d1_np = i_d1_np;
    
    n = nnz(lead_lag_incidence)+M.exo_nbr;
    ih = reshape(1:n*n,n,n);
    i_d2_np = ih(i_d1_np,i_d1_np);
    pm.i_d2_np = i_d2_np(:);

    ih = reshape(1:n*n*n,n,n,n);
    i_d3_np = ih(i_d1_np,i_d1_np,i_d1_np);
    pm.i_d3_np = i_d3_np(:);

    M_np = M;
    M_np.lead_lag_incidence = lead_lag_incidence_np;
    M_np.lead_lag_incidence = lead_lag_incidence_np;
    M_np.endo_nbr = length(v_np);
    M_np.endo_names = M.endo_names(v_np,:);
    dr_np = struct();
    dr_np = set_state_space(dr_np,M_np,options);
    pm.dr_np = dr_np;
    M_np.var_order_endo_names = M_np.endo_names(dr_np.order_var,:);
    pm.M_np = M_np;
    pm.i_fwrd_g = find(lead_lag_incidence_np(lead_index,dr_np.order_var)');    

    i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,:));
    pm.i_fwrd_f1 = i_fwrd_f1;
    n = nnz(lead_lag_incidence)+M.exo_nbr;
    ih = reshape(1:n*n,n,n);
    i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
    pm.i_fwrd_f2 = i_fwrd_f2(:);
    i_fwrd1_f2 = ih(i_fwrd_f1,:);
    pm.i_fwrd1_f2 = i_fwrd1_f2(:);

    ih = reshape(1:n*n*n,n,n,n);
    i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
    pm.i_fwrd_f3 = i_fwrd_f3(:);
    i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
    pm.i_fwrd1_f3 = i_fwrd1_f3(:);
end

function r=ds_static_model(y0,f_h,p0,eq_np,v_np,v_p,endo_nbr,exo_nbr,params)
    ys = zeros(endo_nbr,1);
    ys(v_p) = p0;
    ys(v_np) = y0;
    r = f_h(ys,zeros(exo_nbr,1),params);
    r = r(eq_np);
end

function ghsuu = get_ghsuu(g,ns,nx)
    nxx = nx*(nx+1)/2;
    m1 = 0;
    m2 = ns*(ns+1)/2;
    kk = 1:(nx*nx);
    ghsuu = zeros(size(g,1),(ns*nx*nx));
    
    for i=1:n
        j = m1+(1:m2);
        k = j(end-nxx+1:end);
        ghsuu(:,kk) = unfold2(g(:,k),nx);
        m1 = m1+m2;
        m2 = m2 - (n-i+1);
        kk = kk + nx*nx;
    end
end