diff --git a/matlab/csolve.m b/matlab/csolve.m
new file mode 100644
index 0000000000000000000000000000000000000000..a99f893ae1efb4a06c069cb65757a1d6bf6efed2
--- /dev/null
+++ b/matlab/csolve.m
@@ -0,0 +1,142 @@
+function [x,rc] = csolve(FUN,x,gradfun,crit,itmax,varargin)
+%function [x,rc] = csolve(FUN,x,gradfun,crit,itmax,varargin)
+% FUN should be written so that any parametric arguments are packed in to xp,
+% and so that if presented with a matrix x, it produces a return value of
+% same dimension of x. The number of rows in x and FUN(x) are always the
+% same. The number of columns is the number of different input arguments
+% at which FUN is to be evaluated.
+%
+% gradfun: string naming the function called to evaluate the gradient matrix. If this
+% is null (i.e. just "[]"), a numerical gradient is used instead.
+% crit: if the sum of absolute values that FUN returns is less than this,
+% the equation is solved.
+% itmax: the solver stops when this number of iterations is reached, with rc=4
+% varargin: in this position the user can place any number of additional arguments, all
+% of which are passed on to FUN and gradfun (when it is non-empty) as a list of
+% arguments following x.
+% rc: 0 means normal solution, 1 and 3 mean no solution despite extremely fine adjustments
+% in step length (very likely a numerical problem, or a discontinuity). 4 means itmax
+% termination.
+%---------- delta --------------------
+% differencing interval for numerical gradient
+delta = 1e-6;
+%-------------------------------------
+%------------ alpha ------------------
+% tolerance on rate of descent
+alpha=1e-3;
+%---------------------------------------
+%------------ verbose ----------------
+verbose=0;% if this is set to zero, all screen output is suppressed
+%-------------------------------------
+%------------ analyticg --------------
+analyticg=1-isempty(gradfun); %if the grad argument is [], numerical derivatives are used.
+%-------------------------------------
+nv=length(x);
+tvec=delta*eye(nv);
+done=0;
+if isempty(varargin)
+ f0=feval(FUN,x);
+else
+ f0=feval(FUN,x,varargin{:});
+end
+af0=sum(abs(f0));
+af00=af0;
+itct=0;
+while ~done
+ disp([af00-af0 crit*max(1,af0)])
+ if itct>3 & af00-af0<crit*max(1,af0) & rem(itct,2)==1
+ randomize=1;
+ else
+ if ~analyticg
+ if isempty(varargin)
+ grad = (feval(FUN,x*ones(1,nv)+tvec)-f0*ones(1,nv))/delta;
+ else
+ grad = (feval(FUN,x*ones(1,nv)+tvec,varargin{:})-f0*ones(1,nv))/delta;
+ end
+ else % use analytic gradient
+ grad=feval(gradfun,x,varargin{:});
+ end
+ if isreal(grad)
+ if rcond(grad)<1e-12
+ grad=grad+tvec;
+ end
+ dx0=-grad\f0;
+ randomize=0;
+ else
+ if(verbose),disp('gradient imaginary'),end
+ randomize=1;
+ end
+ end
+ if randomize
+ if(verbose),fprintf(1,'\n Random Search'),end
+ dx0=norm(x)./randn(size(x));
+ end
+ lambda=1;
+ lambdamin=1;
+ fmin=f0;
+ xmin=x;
+ afmin=af0;
+ dxSize=norm(dx0);
+ factor=.6;
+ shrink=1;
+ subDone=0;
+ while ~subDone
+ dx=lambda*dx0;
+ f=feval(FUN,x+dx,varargin{:});
+ af=sum(abs(f));
+ if af<afmin
+ afmin=af;
+ fmin=f;
+ lambdamin=lambda;
+ xmin=x+dx;
+ end
+ if ((lambda >0) & (af0-af < alpha*lambda*af0)) | ((lambda<0) & (af0-af < 0) )
+ if ~shrink
+ factor=factor^.6;
+ shrink=1;
+ end
+ if abs(lambda*(1-factor))*dxSize > .1*delta;
+ lambda = factor*lambda;
+ elseif (lambda > 0) & (factor==.6) %i.e., we've only been shrinking
+ lambda=-.3;
+ else %
+ subDone=1;
+ if lambda > 0
+ if factor==.6
+ rc = 2;
+ else
+ rc = 1;
+ end
+ else
+ rc=3;
+ end
+ end
+ elseif (lambda >0) & (af-af0 > (1-alpha)*lambda*af0)
+ if shrink
+ factor=factor^.6;
+ shrink=0;
+ end
+ lambda=lambda/factor;
+ else % good value found
+ subDone=1;
+ rc=0;
+ end
+ end % while ~subDone
+ itct=itct+1;
+ if(verbose)
+ fprintf(1,'\nitct %d, af %g, lambda %g, rc %g',itct,afmin,lambdamin,rc)
+ fprintf(1,'\n x %10g %10g %10g %10g',xmin);
+ fprintf(1,'\n f %10g %10g %10g %10g',fmin);
+ end
+ x=xmin;
+ f0=fmin;
+ af00=af0;
+ af0=afmin;
+ if itct >= itmax
+ done=1;
+ rc=4;
+ elseif af0<crit;
+ done=1;
+ rc=0;
+ end
+end
diff --git a/matlab/dynare_solve.m b/matlab/dynare_solve.m
index c2fa873429e2a44cffda3093267a520a9c15f87b..f394d1674ae6c1770c076061a585c024537b366f 100644
--- a/matlab/dynare_solve.m
+++ b/matlab/dynare_solve.m
@@ -77,6 +77,8 @@ function [x,info] = dynare_solve(func,x,jacobian_flag,varargin)
end
[x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,varargin{:});
+ elseif options_.solve_algo == 3
+ [x,info] = csolve(func,x,'grad_ss',1e-6,500,varargin{:});
end
% fvec1 = feval(func,x,varargin{:})