I fail to understand this requirement that KKT multipliers be used. Enforcing the needed nonnegativity condition will be difficult or impossible. Since you want an ellipse and your quadratic parameters can be rescaled, it makes more sense just to insist on the equality b^2-4ac=-1. This can be done with a Lagrange multiplier.

A generally better way to fit ellipses to points is from A. Fitzgibbon, M. Pilu, and R. Fisher. "Direct least square fitting of ellipses". IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 476--480, May 1999. Mathematica implementation code can be fond at the link below. http://forums.wolfram.com/mathgroup/archive/2001/Sep/msg00179.html

That's code I wrote to construct the Kuhn-Karush-Tucker conditions and solve them with Reduce. It's somewhat nonstandard as it finds maxima and minima simultaneously. It's designed for small problems which can be handled symbolically.

Did you clear all the variables?

data = Table[{x, 1/x}, {x, Range[6]}];

val = (a x^2 + b*x*y + c y^2 + d*x + e*y + f)^2 /. 
   Table[Thread[{x, y} -> data[[i]]], {i, Length[data]}];

In[16]:= NSolve[
 Thread[D[Plus @@ val + \[Lambda] (b^2 - 4*a*c + 1), {{a, b, c, d, e, 
      f, \[Lambda]}}] == 0], {a, b, c, d, e, f, \[Lambda]}, Reals]

Out[16]= {{a -> 0.071953, b -> 0, c -> 3.47449, d -> -0.922589, 
  e -> -6.58444, 
  f -> 3.96278, \[Lambda] -> 0.00264096}, {a -> -0.071953, b -> 0, 
  c -> -3.47449, d -> 0.922589, e -> 6.58444, 
  f -> -3.96278, \[Lambda] -> 0.00264096}}

You want the solution where f is negative.

I think Grad is a new function in Version 10. Try

In[2]:= NSolve[
 Thread[D[x + y + \[Lambda] (x^2 + y^2 - 1), {{x, y, \[Lambda]}}] == 
   0], {x, y, \[Lambda]}]

Out[2]= {{x -> 0.707107, 
  y -> 0.707107, \[Lambda] -> -0.707107}, {x -> -0.707107, 
  y -> -0.707107, \[Lambda] -> 0.707107}}

Here's an example of finding the minimum and maximum points for an objective function = x + y and a single equality constraint x^2 + y^2=1

In[42]:= NSolve[
 Thread[Grad[x + y + \[Lambda] (x^2 + y^2 - 1), {x, y, \[Lambda]}] == 
   0], {x, y, \[Lambda]}]

Out[42]= {{x -> 0.707107, 
  y -> 0.707107, \[Lambda] -> -0.707107}, {x -> -0.707107, 
  y -> -0.707107, \[Lambda] -> 0.707107}}

The KKT equations extend Lagrange multipliers to inequality constraints by adding a requirement that the multiplier for an inequality constraint be zero if the constraint is not active. This is done by requiring that the product of the multiplier and the constraint value be zero. Therefore using the constraint b^2-4ac=-1 with a Lagrange multiplier is equivalent to the KKT equations, in the special case that there are no inequality constraints.

My advice is to use a Lagrange multiplier and call it a KKT multiplier. Nobody will be any the wiser. The Lagrange multiplier, used as I noted, will give ellipses.

"i need generate ellipse alaways from cloud of points"

I am aware of that. Did you read my response from 10 or so hours prior to yours? It mentions two ways for enforcing ellipses.

want to make my application was intact, and add in the menu KKT. That's the part I have to take in the menu?

 nearCoords = nearpoints[[All, 1]];
     {xs, ys} = Transpose[nearCoords];
     newellipse = aa*x^2 + 2*bb*x*y + cc*y^2 + 2*dd*x + 2*ff*y + gg;
     distance = Plus @@ (newellipse^2 /. {x -> xs, y -> ys});
     {res, coes} = NMinimize[distance, {aa, bb, cc, dd, ff, gg}];
     scaleup = FromDigits[{{1}, Last@RealDigits[1/(gg /. coes)] + 1}];
     esolve = Expand[scaleup*(newellipse /. coes)];

I suspect your professor is not going to be satisfied with your using the code I wrote. For one thing, I wrote the code to be as compact as possible, not as understandable as possible. I also wrote it so that the subscripts on the multipliers are the constraints they refer to. This is something that is easily done in Mathematica but very nonstandard. Since there is only one constraint, constructing the KKT equations is straightforward. I suggest you do that yourself for your code.

because it requires me to do my professor

if someone help me to understand this I managed to add it to my application?

 consconvrule = {
    x_ >= y_ -> y - x,
    x_ == y_ -> x - y ,
    x_ <= y_ -> x - y,
    lb_ <= x_ <= ub_ -> (x - lb) (x - ub),
    ub_ >= x_ >= lb_ -> (x - lb) (x - ub)
    };

 KKT[obj_, cons_List, vars_List] :=
 Module[
  {convcons, gradobj, gradcons, ?},
  convcons = (cons /. consconvrule);
  {gradobj, gradcons} = D[{obj, convcons}, {vars}];
  ? = Subscript[?, #] & /@ cons;
  LogicalExpand @ Reduce[
    Flatten[{
      Thread[gradobj == ?.gradcons],
      Thread[?*convcons == 0] /.
       Subscript[?, Equal[a_, b_]] -> 0,
      cons,
      {objval == obj}
      }],
    Join[{objval}, vars, ?],
    Reals,
    Backsubstitution -> True
    ]
  ]

to this application

Off[NMinimize::eit]; Off[NMinimize::lstol];
DynamicModule[{rng, ptk, szumx, szumy, final}, 
 Panel[Column[{"Theta z przedzia?u od 0 do:", 
    RadioButtonBar[
     Dynamic[rng], {2 Pi -> "2\[Pi]", Pi -> "\[Pi]", 
      Pi/2 -> "\[Pi]/2", Pi/8 -> "\[Pi]/8"}], , 
    "Ilo?? generowanych punktów:", 
    RadioButtonBar[
     Dynamic[ptk], {5 -> "5", 20 -> "20", 50 -> "50", 
      100 -> "100"}], , "Warto?ci wektora przesuni?cia", 
    "Warto?? xi:" RadioButtonBar[
      Dynamic[szumx], {-0.1 -> "-0.1", -.5 -> "-0.5", -.9 -> 
        "-0.9", -3 -> "-3"}], 
    "Warto?? yj:" RadioButtonBar[
      Dynamic[szumy], {0.1 -> "0.1", .5 -> "0.5", .9 -> "0.9", 
       3 -> "3"}], 
    Button["Generuj Elipse", 
     While[True,(*Randomly choose coefficients until acceptable*){a, 
        b, c, d, f, g} = RandomReal[{-10, 10}, 6];
      \[CapitalDelta] = -c d^2 + 2 b d f - a f^2 - b^2 g + a c g;
      j = -b^2 + a c; i = a + c;
      If[\[CapitalDelta] != 0 && j > 0 && \[CapitalDelta]/i < 0, 
       Break[]]];
     ellipse = a*x^2 + 2*b*x*y + c*y^2 + 2*d*x + 2*f*y + g;
     (*Center of an ellipse in general form is{(c d-b f)/(b^2-
     a c),(a f-b d)/(b^2-a c)}*)
     points = Table[theta = RandomReal[{0, rng}];
       ksol = 
        FindRoot[(ellipse /. {x -> 
              k*Cos[theta] + (c d - b f)/(b^2 - a c), 
             y -> k*Sin[theta] + (a f - b d)/(b^2 - a c)}) == 0, {k, 
          1.}];
       Point[{x, y}] /. {x -> k*Cos[theta] + (c d - b f)/(b^2 - a c), 
          y -> k*Sin[theta] + (a f - b d)/(b^2 - a c)} /. ksol, {ptk}];
     nearpoints = 
      points /. 
       Point[{x_, y_}] :> 
        Point[{x + RandomReal[{szumx, szumy}], 
          y + RandomReal[{szumx, szumy}]}];
     (*ellipse x and y min and max values*)
     yplotrange = 
      Flatten[{y, 
        Sort[{(2*b*d - 2*a*f + 
             Sqrt[(2*b*d - 2*a*f)^2 - 
               4*(b^2 - a*c)*(d^2 - a*g)])/(2*(-b^2 + a*c)), (-2*b*
              d + 2*a*f + 
             Sqrt[(2*b*d - 2*a*f)^2 - 
               4*(b^2 - a*c)*(d^2 - a*g)])/(2*(b^2 - a*c))}]}];
     xplotrange = 
      Flatten[{x, 
        Sort[{(2*c*d - 2*b*f + 
             Sqrt[(-2*c*d + 2*b*f)^2 - 
               4*(b^2 - a*c)*(f^2 - c*g)])/(2*(b^2 - a*c)), (-2*c*d + 
             2*b*f + 
             Sqrt[(-2*c*d + 2*b*f)^2 - 
               4*(b^2 - a*c)*(f^2 - c*g)])/(2*(-b^2 + a*c))}]}];
     (*minimize distance of near points to a new general ellipse*)
     nearCoords = nearpoints[[All, 1]];
     {xs, ys} = Transpose[nearCoords];
     newellipse = aa*x^2 + 2*bb*x*y + cc*y^2 + 2*dd*x + 2*ff*y + gg;
     distance = Plus @@ (newellipse^2 /. {x -> xs, y -> ys});
     {res, coes} = NMinimize[distance, {aa, bb, cc, dd, ff, gg}];
     scaleup = FromDigits[{{1}, Last@RealDigits[1/(gg /. coes)] + 1}];
     esolve = Expand[scaleup*(newellipse /. coes)];
     final = 
      Show[ContourPlot[{ellipse == 0, esolve == 0}, 
        Evaluate[xplotrange], Evaluate[yplotrange]], Graphics[points],
        Graphics[{Red, nearpoints}], ImageSize -> {500, Automatic}];],
     Dynamic@final
    (*,Dynamic@Grid[Join[{{"points","nearby points"}},
    Transpose[{points,nearpoints}][[All,All,1]]],Frame->All,
    Alignment->Left]*)}]]]