MaPal93

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These are questions asked by MaPal93

Hi,

I want to analyze a quartic equation: rho-analysis.mw. I am interested in positive roots, which I need expressed in explicit/closed form.

I include four questions in the script.

Thanks!

EDIT (since my question was originally tagged as incomplete and even duplicate):
Above all I am trying to understand:

  1. Why implicitplot3d returns a unique strictly positive root for all rho and Gamma but plot() of the quartic evaluated for a specific pair of value returns three positive roots?
  2. rho is a correlation coefficient, thus bounded in (-1,+1): how to adjust the precision of the slider in Explore() so that I can play around with multiple rho values within said range?

I have two expressions, wo_theta and with_theta, which depend on multiple variables.

I would need your help to:

  1. Verify, as formally as possible, that wo_theta > with_theta always, i.e., for any value of theta different from zero (and regardless of the values taken up by the other variables)
  2. Show the above in a way that is easy and immediate to interpret (perhaps using some type of plot?)

In other words, I want to verify that as soon as I introduce any theta in my expression such expression becomes smaller:

restart;

local gamma;

gamma

(1)

assume(0 < gamma, 0 < nu__02, 0 < nu__01, 0 <= sigma__v, delta__1::real, delta__2::real, delta__3::real, theta::real);
interface(showassumed=0);

1

(2)

wo_theta := X__3*(-X__3*lambda__3 - delta__3*lambda__3 + DEV) + X__2*(-X__2*lambda__2 - delta__2*lambda__2 - nu__02) + X__1*(-X__1*lambda__1 - delta__1*lambda__1 - nu__01) + X__2*(nu__02 + DEV/2) + X__1*(nu__01 + DEV/2) - gamma*X__2^2*sigma__v^2/4 - gamma*X__1^2*sigma__v^2/4 + gamma*X__2*X__1*sigma__v^2/2;

X__3*(-X__3*lambda__3-delta__3*lambda__3+DEV)+X__2*(-X__2*lambda__2-delta__2*lambda__2-nu__02)+X__1*(-X__1*lambda__1-delta__1*lambda__1-nu__01)+X__2*(nu__02+(1/2)*DEV)+X__1*(nu__01+(1/2)*DEV)-(1/4)*gamma*X__2^2*sigma__v^2-(1/4)*gamma*X__1^2*sigma__v^2+(1/2)*gamma*X__2*X__1*sigma__v^2

(3)

with_theta := X__3*(-X__3*lambda__3 - theta*lambda__3 - delta__3*lambda__3 + DEV) + X__2*(-X__2*lambda__2 + theta*lambda__2 - delta__2*lambda__2 - nu__02) + X__1*(-X__1*lambda__1 + theta*lambda__1 - delta__1*lambda__1 - nu__01) + X__2*(nu__02 + DEV/2) + X__1*(nu__01 + DEV/2) - gamma*X__2^2*sigma__v^2/4 - gamma*X__1^2*sigma__v^2/4 + gamma*X__2*X__1*sigma__v^2/2 + theta*(lambda__1*(X__1 + delta__1 - theta) + lambda__2*(X__2 + delta__2 - theta) - lambda__3*(X__3 + delta__3 + theta));

X__3*(-X__3*lambda__3-theta*lambda__3-delta__3*lambda__3+DEV)+X__2*(-X__2*lambda__2+theta*lambda__2-delta__2*lambda__2-nu__02)+X__1*(-X__1*lambda__1+theta*lambda__1-delta__1*lambda__1-nu__01)+X__2*(nu__02+(1/2)*DEV)+X__1*(nu__01+(1/2)*DEV)-(1/4)*gamma*X__2^2*sigma__v^2-(1/4)*gamma*X__1^2*sigma__v^2+(1/2)*gamma*X__2*X__1*sigma__v^2+theta*(lambda__1*(X__1+delta__1-theta)+lambda__2*(X__2+delta__2-theta)-lambda__3*(X__3+delta__3+theta))

(4)

collect(with_theta, theta);

(-lambda__1-lambda__2-lambda__3)*theta^2+(-X__3*lambda__3+X__2*lambda__2+X__1*lambda__1+lambda__1*(X__1+delta__1)+lambda__2*(X__2+delta__2)-lambda__3*(X__3+delta__3))*theta+X__3*(-X__3*lambda__3-delta__3*lambda__3+DEV)+X__2*(-X__2*lambda__2-delta__2*lambda__2-nu__02)+X__1*(-X__1*lambda__1-delta__1*lambda__1-nu__01)+X__2*(nu__02+(1/2)*DEV)+X__1*(nu__01+(1/2)*DEV)-(1/4)*gamma*X__2^2*sigma__v^2-(1/4)*gamma*X__1^2*sigma__v^2+(1/2)*gamma*X__2*X__1*sigma__v^2

(5)

solve(wo_theta > with_theta, theta) assuming 0 < gamma, 0 < nu__02, 0 < nu__01, 0 < sigma__v, delta__1::real, delta__2::real, delta__3::real, theta::real;

solve(with_theta < wo_theta, theta);

Warning, solve may be ignoring assumptions on the input variables.

 

Warning, solutions may have been lost

 

difference_term := (-lambda__1 - lambda__2 - lambda__3)*theta^2 + (X__1*lambda__1 + X__2*lambda__2 - X__3*lambda__3 + lambda__1*(X__1 + delta__1) + lambda__2*(X__2 + delta__2) - lambda__3*(X__3 + delta__3))*theta;

(-lambda__1-lambda__2-lambda__3)*theta^2+(-X__3*lambda__3+X__2*lambda__2+X__1*lambda__1+lambda__1*(X__1+delta__1)+lambda__2*(X__2+delta__2)-lambda__3*(X__3+delta__3))*theta

(6)

# I would expect such difference_term in theta to be always < 0, i.e., for any theta different from 0)
# (Note that lambda_1, lambda_2, and lambda_3 are always > 0, while theta, the three X and the three delta can be positive or negative. In other words, it suffices to show that the linear term in theta is always negative...)
solve(difference_term<0);

Warning, solve may be ignoring assumptions on the input variables.

 

{X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, theta < 0, lambda__1 < 0, lambda__2 < 0, lambda__3 < 0}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, X__2 < (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2, theta < 0, lambda__2 < 0, lambda__3 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__1, theta < 0, lambda__2 < 0, lambda__3 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, X__1 < (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1, theta < 0, lambda__1 < 0, lambda__3 < 0}, {X__1 = X__1, X__2 = X__2, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__2 = 0, theta < 0, lambda__3 < 0, -(1/2)*delta__3-(1/2)*theta < X__3}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < lambda__1, theta < 0, lambda__3 < 0, (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__2, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, theta < 0, lambda__1 < 0, lambda__3 < 0}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < lambda__2, theta < 0, lambda__3 < 0, (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2 < X__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__1, 0 < lambda__2, theta < 0, lambda__3 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, X__1 < -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1, theta < 0, lambda__1 < 0, lambda__2 < 0}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__3 = 0, X__2 < -(1/2)*delta__2+(1/2)*theta, theta < 0, lambda__2 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < lambda__1, theta < 0, lambda__2 < 0, -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, lambda__3 = 0, X__1 < -(1/2)*delta__1+(1/2)*theta, theta < 0, lambda__1 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, lambda__3 = 0, 0 < lambda__1, theta < 0, -(1/2)*delta__1+(1/2)*theta < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < lambda__2, X__1 < -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1, theta < 0, lambda__1 < 0}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__3 = 0, 0 < lambda__2, theta < 0, -(1/2)*delta__2+(1/2)*theta < X__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < lambda__1, 0 < lambda__2, theta < 0, -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__3, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, theta < 0, lambda__1 < 0, lambda__2 < 0}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < lambda__3, X__2 < (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2, theta < 0, lambda__2 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__1, 0 < lambda__3, theta < 0, lambda__2 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < lambda__3, X__1 < (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1, theta < 0, lambda__1 < 0}, {X__1 = X__1, X__2 = X__2, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__2 = 0, 0 < lambda__3, X__3 < -(1/2)*delta__3-(1/2)*theta, theta < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < lambda__1, 0 < lambda__3, theta < 0, (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__2, 0 < lambda__3, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, theta < 0, lambda__1 < 0}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < lambda__2, 0 < lambda__3, theta < 0, (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2 < X__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < lambda__1, 0 < lambda__2, 0 < lambda__3, theta < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, lambda__1 < 0, lambda__2 < 0, lambda__3 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < theta, lambda__2 < 0, lambda__3 < 0, (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2 < X__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__1, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, lambda__2 < 0, lambda__3 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < theta, lambda__1 < 0, lambda__3 < 0, (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1 < X__1}, {X__1 = X__1, X__2 = X__2, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__2 = 0, 0 < theta, X__3 < -(1/2)*delta__3-(1/2)*theta, lambda__3 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < theta, 0 < lambda__1, X__1 < (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1, lambda__3 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__2, lambda__1 < 0, lambda__3 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < theta, 0 < lambda__2, X__2 < (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2, lambda__3 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__1, 0 < lambda__2, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, lambda__3 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < theta, lambda__1 < 0, lambda__2 < 0, -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1 < X__1}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__3 = 0, 0 < theta, lambda__2 < 0, -(1/2)*delta__2+(1/2)*theta < X__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < theta, 0 < lambda__1, X__1 < -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1, lambda__2 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, lambda__3 = 0, 0 < theta, lambda__1 < 0, -(1/2)*delta__1+(1/2)*theta < X__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, lambda__3 = 0, 0 < theta, 0 < lambda__1, X__1 < -(1/2)*delta__1+(1/2)*theta}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < theta, 0 < lambda__2, lambda__1 < 0, -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1 < X__1}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__3 = 0, 0 < theta, 0 < lambda__2, X__2 < -(1/2)*delta__2+(1/2)*theta}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__3 = 0, 0 < theta, 0 < lambda__1, 0 < lambda__2, X__1 < -(1/2)*(2*X__2*lambda__2-lambda__1*theta-lambda__2*theta+delta__1*lambda__1+delta__2*lambda__2)/lambda__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__3, lambda__1 < 0, lambda__2 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < theta, 0 < lambda__3, lambda__2 < 0, (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2 < X__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__1, 0 < lambda__3, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1, lambda__2 < 0}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < theta, 0 < lambda__3, lambda__1 < 0, (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1 < X__1}, {X__1 = X__1, X__2 = X__2, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, lambda__2 = 0, 0 < theta, 0 < lambda__3, -(1/2)*delta__3-(1/2)*theta < X__3}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__2 = 0, 0 < theta, 0 < lambda__1, 0 < lambda__3, X__1 < (1/2)*(2*X__3*lambda__3+lambda__1*theta+lambda__3*theta-delta__1*lambda__1+delta__3*lambda__3)/lambda__1}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__2, 0 < lambda__3, lambda__1 < 0, -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1 < X__1}, {X__1 = X__1, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, lambda__1 = 0, 0 < theta, 0 < lambda__2, 0 < lambda__3, X__2 < (1/2)*(2*X__3*lambda__3+lambda__2*theta+lambda__3*theta-delta__2*lambda__2+delta__3*lambda__3)/lambda__2}, {X__2 = X__2, X__3 = X__3, delta__1 = delta__1, delta__2 = delta__2, delta__3 = delta__3, 0 < theta, 0 < lambda__1, 0 < lambda__2, 0 < lambda__3, X__1 < -(1/2)*(2*X__2*lambda__2-2*X__3*lambda__3-lambda__1*theta-lambda__2*theta-lambda__3*theta+delta__1*lambda__1+delta__2*lambda__2-delta__3*lambda__3)/lambda__1}

(7)
 

NULL

Download inequality.mw

Details here: non-dimensionalization.mw. Thanks!

In short, I want to plot all the ten roots of a 10th-degree polynomial in _Z with 4 or 5 primitive parameters. To do so, I need to find that combination of parameters that allows me to transform/scale/non-dimensionalize the original polynomial in a polynomial in _Z with just (1) one parameter or at maximum (2) two parameters. In the case of (1), I can plot its roots in a standard 2D plot against the single parameter. In the case of (2), I can plot the roots in a standard 3D plot against the two parameters.

I want to export plots as PNG but found in the past that when using commands to automate the process (and explicitly controlling for image quality) some symbolic notation on the axes or in the plots themselves are translated to 1D.

Anyway, regardless of the reason just provided, I have a preference for exporting plots as PNG manually rather than automatically in some of my scripts. How to do this while ensuring the best quality? By default, manual exports into PNG have quite bad quality. 

How to interpret the output to limit()?

restart;
local gamma;

gamma

(1)

A := -sigma__v^2*(((-2*gamma^2*sigma__d^4*sigma__e^4 - 16)*sigma__v^6 + (-6*gamma^2*sigma__d^4*sigma__e^6 - 4*gamma^2*sigma__d^2*sigma__e^4 - 48*sigma__e^2)*sigma__v^4 - sigma__e^4*(gamma^4*sigma__d^6*sigma__e^6 + 4*gamma^2*sigma__d^4*sigma__e^4 + 8*gamma^2*sigma__d^2*sigma__e^2 + 48)*sigma__v^2 - 4*gamma^2*sigma__d^2*sigma__e^8 - 16*sigma__e^6)*sqrt(gamma^2*sigma__d^2*sigma__e^4 + 4*sigma__e^2 + 4*sigma__v^2) + (2*sigma__d^2*sigma__v^8 + (12*sigma__d^2*sigma__e^2 + 8)*sigma__v^6 + 2*(12 + gamma^2*sigma__d^4*sigma__e^4 + sigma__d^2*(gamma^2 + 13)*sigma__e^2)*sigma__e^2*sigma__v^4 + 8*(3 + gamma^2*sigma__d^4*sigma__e^4 + sigma__d^2*(gamma^2 + 6)*sigma__e^2/2)*sigma__e^4*sigma__v^2 + sigma__e^6*(gamma^2*sigma__d^2*sigma__e^2 + 4)*(gamma^2*sigma__d^4*sigma__e^4 + 2*sigma__d^2*sigma__e^2 + 2))*sigma__v^2*gamma*sigma__d)*sigma__d/(4*(sigma__e^2 + sigma__v^2)^2*(gamma^2*sigma__d^2*sigma__e^4 + 4*sigma__e^2 + 4*sigma__v^2)^2);

-(1/4)*sigma__v^2*(((-2*gamma^2*sigma__d^4*sigma__e^4-16)*sigma__v^6+(-6*gamma^2*sigma__d^4*sigma__e^6-4*gamma^2*sigma__d^2*sigma__e^4-48*sigma__e^2)*sigma__v^4-sigma__e^4*(gamma^4*sigma__d^6*sigma__e^6+4*gamma^2*sigma__d^4*sigma__e^4+8*gamma^2*sigma__d^2*sigma__e^2+48)*sigma__v^2-4*gamma^2*sigma__d^2*sigma__e^8-16*sigma__e^6)*(gamma^2*sigma__d^2*sigma__e^4+4*sigma__e^2+4*sigma__v^2)^(1/2)+(2*sigma__d^2*sigma__v^8+(12*sigma__d^2*sigma__e^2+8)*sigma__v^6+2*(12+gamma^2*sigma__d^4*sigma__e^4+sigma__d^2*(gamma^2+13)*sigma__e^2)*sigma__e^2*sigma__v^4+8*(3+gamma^2*sigma__d^4*sigma__e^4+(1/2)*sigma__d^2*(gamma^2+6)*sigma__e^2)*sigma__e^4*sigma__v^2+sigma__e^6*(gamma^2*sigma__d^2*sigma__e^2+4)*(gamma^2*sigma__d^4*sigma__e^4+2*sigma__d^2*sigma__e^2+2))*sigma__v^2*gamma*sigma__d)*sigma__d/((sigma__e^2+sigma__v^2)^2*(gamma^2*sigma__d^2*sigma__e^4+4*sigma__e^2+4*sigma__v^2)^2)

(2)

# Limits

A__0 := limit(A, gamma = 0);
A__inf_wo_assumptions := limit(A, gamma = infinity);
A__inf_with_assumptions := limit(A, gamma = infinity) assuming 0 < sigma__e, 0 < sigma__v, 0 < sigma__d;

(1/2)*sigma__v^2*sigma__d/(sigma__e^2+sigma__v^2)^(1/2)

 

signum(sigma__d^3*sigma__e^2*sigma__v^4*(-sigma__d*sigma__e^2+(sigma__d^2*sigma__e^4)^(1/2))/(sigma__e^2+sigma__v^2)^2)*infinity

 

0

(3)

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