Hilbert-Robinson-Reznick-Blekherman positive polynomials

Reznick (2007) (see previous post) simplified Robinson simplification of Hilbert construction of positive polynomials that are not sums of squares by his perturbation lemma. Blekherman shows that the mystery is in the dimension of monomial basis. For example, there are 2^n points in n-dimensional hypercube, but since there are only \frac{(n+1)n}{2} quadratic monomials all this points when evaluated cannot have rank more then the number of monomials. As a consequence, it is sufficient to nullify only about quaratic number of points in a quadratic form (by applying a set of linear constraints on the coefficients arising from evaluations of values of monomials) to nullify all points in a hypercube for these quadratic forms.

There are much more monomials in quartic polynomials (much larger basis), and therefore much larger freedom in choosing coefficients such that quartic will be nullified at quadratic number of points in hypercube, but will be nonzero on other points of a hypercube! That lead that such quartic polynomial even if it will be positive will not be a sum of squares.

Reznick perturbation lemma open a way to automatically (algorithmically) generate positive polynomials that a re not sums of squares, which in the current version may not lead to sufficient expansion of positive polynomial cone to solve all the problems from a given class. Reznick (2007) perturbation lemma, simplified for our current purpose, in high school terms, starts from a sums of squares of terms defining hypercube, say \{0,1\}^n is f(x)= \sum \limits_{k=1}^N x_k^2(x_k-1)^2. Moreover, at the minimum it round, i.e. Hessian is strictly positive definite (easy to check by direct calculations, intuitively each term in the sum around the minimum is a positive definite quadratic multiplied by approximately 1). Now we are going to perturb this quartic by another quartic (g(x)), splitting points in the hypercube into two sets. In one set we require that Taylor expansion of g(x) vanish to second order term, and on the other set we require that g(x) is strictly positive. Only those points in the hypercube are dangerous, since perturbation f(x)+ c g(x), c>0 may lead to negative values around minima for any positive c. In the rest of the space we can choose small enough c to ensure positiveness of resulting polynomial (I skip this part, but one need to have worst estimate for the values of g(x) for a given norm of x and take a region bounded by some norm and outer region where only ratios between leading coefficients matter). For the first set of points g(x) has Hessians with the finite eigenvalues. If we need to ensure that c times minimum eigenvalue is smaller that Hessian eigenvalues (all the same) for f(x) in the minima. That ensures 0 value on the set that are local minima. For the second set of points the value is positive in some neighborhood by construction.

Now automatic construction: given monomial basis we need to ensure about quadratic number of points to have 0 values and gradients being 0. That is about n^3 conditions for about n^4 monomials. The rest of points in the hypercube should be strictly greater than zero. For that we need to ensure that the values at the basis points are positive and that all point in the hypercube are represented by the linear combinations with positive coefficients. For that we need to peak as the basis points the points with minimum number of ‘1’ as coordinates. and the second condition is the inequality condition. leading to linear program. It has solution because we have coefficients in front of x_k^2 that can grow unbound, and being compensated by other terms. and inequality condition is much weaker then equality condition. Then one can compute the estimate for the constant c, which depends only on the calculations in the basis points. The exact machinery still need to be worked out, since one need also estimate the values of projections of all point of hypercube onto basis points.

That may be not sufficient to produce wide enough expansion of SOS cone to solve all instances of, say, partition problem, but we may need small enough extension to pertube problem quartic to be represented by the sums of squares.

Sincerely yours,

psd that is not SOS:
x_1^2 (x_1 - 1)^2 + x_2^2 (x_2 - 1)^2 + x_3^2(x_3 - 1)^2 + \frac{1}{1000} \left( 3 x_2 (x_2 - 1) ( 28 x_1^2 - 28 x_1 - 19 x_2^2 + 19 x_2 + 28 x_3^2 - 28 x_3) + 140 x_1 x_2 x_3 (x_1 + x_2 + x_3 - 2)\right)

and the (not cleaned) code in Matlab with symbolic toolbox to get it

%% mupad test for positive polynomials Hilber-Reznik

y= evalin( symengine, sprintf( 'combinat::compositions(4, Length= %d, MinPart=0)', n+1) );
pow= arrayfun( @(k) double(y(k)), 1:numel(y), 'UniformOutput', false );
pow= cat(1, pow{:});
xs= sym( 'x', [n,1]);

xx= zeros( 2^n, n );
for k=1:n,
xx( bitand(v, 2^(k-1) )~=0 , k )=1;
%xx= 2*xx-1;
xx(:, n+1)=1;
%xx( end+1, :)= [1 2 2 1];

y= evalin( symengine, sprintf( 'combinat::compositions(2, Length= %d, MinPart=0)', n+1) );
pow2= arrayfun( @(k) double(y(k)), 1:numel(y), 'UniformOutput', false );
pow2= cat(1, pow2{:});

yy2= nan( size( pow2,1), size(xx,2) );
yy= nan( size( pow,1), size(xx,2) );
for k=1:size(xx,1),
yy(:, k)= prod( bsxfun( @power, xx(k,:), pow ), 2);
yy2(:, k)= prod( bsxfun( @power, xx(k,:), pow2 ), 2);

for k=size( pow, 1 ):-1:1,
xsm( k )= sym(1);
for k2= n:-1:1,
xsm( k )= xsm( k )*xs(k2)^pow( k, k2 );

for k= n:-1:1,
dxsm(k,:)= diff( xsm, xs(k) );

r= rank(yy2);
x0Idx= 1:7;
x1Idx= 8;

%we need also condition for gradient here
dpow= zeros(size(pow));
for k=1: size( pow, 2)-1,
idx= pow(:,k)>0;
dpow( idx, end )= dpow(idx, end).*pow(idx,k);
dpow(idx,k)= pow(idx,k)-1;
% %
% equality constraints
lc= zeros(size(pow, 1), 0);
for k=1:7,
lc(:, end+1)= subs( xsm, xs, xx(k, 1:3) )';
lc(:, end+(1:n))= subs( dxsm, xs, xx(k, 1:3) )';
% lc(:, end+(1:n))= subs( dxsm, xs, xx(8, 1:3) )';

% The following line is a place to play with different polynomials
% we have rather strange way to do it here by imposing eight point
% where quartic polynomial vanishes first order term on Taylor expansion,
% and the value at this point is positive (it is sufficient to have 0 here,
% but the code would be more complicated).
% removing second line below (lc .. ) and switching next lc lines
% (commenting/ uncomenting) lead to Robinson polynomial

x2= [3 -3 3]; % [3 -1 3] also works well
lc(:, end+(1:n))= subs( dxsm, xs, x2 )'; % comment for Robinson
bc= zeros( size( lc, 2), 1 );

% lc(:, end+1)= subs( xsm, xs, xx(8, 1:3) )'; % uncomment for Robinson
lc(:, end+1)= subs( xsm, xs, x2 )'; % comment for Robinson


cf= mldivide( lc', bc)*54*2 % multiplier 1 for Robinson
disp( [size(lc) rank(lc)])

% %

% coefficient c in perturbation lemma was found by hand, and in now way is optimal
p2= 1/1000*sum( round(cf).*xsm.') + sum( xs.^2.*(xs-1).^2) % first factor is 1 for Robinson
dp2= [diff(p2, xs(1)) diff(p2, xs(2)) diff(p2, xs(3))]
subs( p2, xs, xx(8, 1:3))
sol= solve(dp2)
xv= ([sol.x1 sol.x2 sol.x3]);
vf= arrayfun( @(k) double(subs( p2, xs, xv(k, :))), 1:size(xv,1))';
[min(vf) min(real(vf)) min(imag(vf))]

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2 Responses to Hilbert-Robinson-Reznick-Blekherman positive polynomials

  1. Pingback: Generating positive polynomials that are not sums of squares | Crackpot Trisector Notes

  2. Pingback: Why P could be equal NP | Crackpot Trisector Notes

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