consider quadratic equation ax^2+bx+c =0 :the coefficients vary between -1 and +1 . just like this :-1<a<+1 , -1<b<+1 , -1<c<+1 ;how can some one proove that this equation should have real answers ?! can anybody help ? thanks in advance.

What is the right syntax to solve :

min {sum(i=1 to 10) sum(j=1..10) (a_i_j)*(x_i)*(x_j)

s.t sum(i=1..n) (b_i)*x_i=p and sum(i=1..n)x_i=1

if a_i_j is a constant, b_i is a constant, p is a constant and x_i, x+j are the decision variables?

I understand that this is a quadratic programming problem and an application of Markowitz optimization. I've tried to use the in-built minimize function but haven't got the right output.

I am trying to produce a worksheet that will give me the two roots of a quadratic equation.

This is what I have:

As you can see, It's not giving me the right answers. What do I do?

Thanks,

Paul

I have been solved a optimization problem by

Hi,

when i tried to solve the following equation, i received ""Warning, solutions may have been lost"":

See attached. I have stared at this long enough to the point I need some FRESH eyes on this. Why does MAPLE return FALSE on the last 2 statements

Write a procedure that determines the solutions of a quadratic equation from inputs a, b and c by using the discriminant and the quadratic formula. The quadratic equation procedure should be able to solve all cases: invalid input, linear case, real and complex roots. The procedure should also plot the given equation.

Question :

Write a procedure that determines the solutions of a quadratic equation from inputs , and by using the discriminant and the quadratic formula.

The quadratic equation procedure should be able to solve all cases: invalid input, linear case, real and complex roots. The procedure should also plot the given equation.

I dont understand how to do this can someone please please do it this for me urgently required.

If I run the below code which finds the optimal portfolio weights given a portfoliotarget return G then I get a nice solution:restart: with(Optimization): with(LinearAlgebra): n := 4: C := 10000: G := .1*C: X := Vector[column]([seq(x[i], i = 1 .. n)]):ER := Vector[column]([0.5e-1, -.20, .15, .30]):Q := Matrix([[0.8e-1, -0.5e-1, -0.5e-1, -0.5e-1], [-0.5e-1, .16, -0.2e-1, -0.2e-1], [-0.5e-1, -0.2e-1, .35, 0.6e-1], [-0.5e-1, -0.2e-1, 0.6e-1, .35]]):

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