Standard Quadratic Optimization with linear constratint

Nonconvex
1 
Q=[

         0    0.8797    0.9302    0.9628    0.8300    0.0000    0.0000    0.0000    0.0000    0.0000;
    0.8797         0    0.9912    0.7415    0.8794    0.0000    0.0000    0.0000    0.0000    0.0000;
    0.9302    0.9912         0    0.8132    0.9033    0.0000    0.0000    0.0000    0.0000    0.0000;
    0.9628    0.7415    0.8132         0    0.7555    0.0000    0.0000    0.0000    0.0000    0.0000;
    0.8300    0.8794    0.9033    0.7555         0    0.0000    0.0000    0.0000    0.0000    0.0000;
    0.0000    0.0000    0.0000    0.0000    0.0000         0    0.8216    0.9995    0.8781    0.8874;
    0.0000    0.0000    0.0000    0.0000    0.0000    0.8216         0    0.8365    0.8695    0.9543;
    0.0000    0.0000    0.0000    0.0000    0.0000    0.9995    0.8365         0    0.8825    0.8965;
    0.0000    0.0000    0.0000    0.0000    0.0000    0.8781    0.8695    0.8825         0    0.9754;
    0.0000    0.0000    0.0000    0.0000    0.0000    0.8874    0.9543    0.8965    0.9754         0];
e=ones(n,1);
cvx_begin
    variable x(n,1); 
    maximize(quad_form(x,Q) );
    subject to
    e'*x == 1;
    x >= 0
    x<=1
    cvx_end
pstar = cvx_optval;

%/////////////
Error using cvx/quad_form (line 211)
The second argument must be positive or negative semidefinite.
%//////////

when I change it to maximize(-quad_form(x,Q) ); and the value of diagonal Q set to one ,it works but the result is wrong.
Can anyone help me???

2

CVX is for convex programs. In order to maximize a quadratic form in CVX, it must be concave, which means that Q must be negative semidefinite. CVX cannot solve your problem.