The inverse of covariance matrix in Markowitz optimization

I’m doing a portfolio optimization problem but the covariance matrix is not of full rank. However, the portopt function does figure out the efficient frontier, while the code I made following mean-variance equation can’t get that. The warning is that “Matrix is close to singular or badly scaled.” Could anyone tell me how to improve the code? Thanks a lot.

%%%code using portopt function  
  load('SP500.mat')
  SPT=SP500';
  SP=SPT(~any(isnan(SPT),2),:);
  SP=SP';
  Ret=price2ret(SP);
  Stocks=Ret(:,2:end);
  Index=Ret(:,1);
  R=mean(Stocks);
  Cov=cov(Stocks);
  Std=std(Stocks);
  portopt(R,Cov,30)
  hold on
  plot(Std,R,'.r')
  plot(std(Index),mean(Index),'*k')
  legend('Efficient Frontier','Individual Stocks','S&P 500')

%%%code using mean-variance equation
load('SP500.mat')
SPT=SP500';
SP=SPT(~any(isnan(SPT),2),:);
SP=SP';
Ret=price2ret(SP);
Stocks=Ret(:,2:end);
Index=Ret(:,1);
R=mean(Stocks);
Cov=cov(Stocks);
Std=std(Stocks);
Inv=inv(Cov);
One=ones(size(R,2),1);
a=R*Inv*R';
b=R*Inv*One;
c=One'*Inv*One;
x=0:0.005:0.25;
y=sqrt((c*x.^2-2*b*x+a)/(a*c-b^2));
plot(y,x)