# Solve using KKT conditions when number of variables is less than number of constraints

\begin{align} \text{minimize} \quad & f(x) = x_1^2 + x_2^2 +x_3^2 \\ \text{subject to} \quad & 2x_1+ x_2-5\leq 0 \\ & x_1+x_3-2\leq 0 \\ & 1-x_1 \leq 0 \\ & 2-x_2 \leq 0 \\ & -x_3 \leq 0 \end{align}

I came across this question of NLPP which apparently has greater number of constraints (other than non negative restrictions) compared to the number of variables involved. My text book states the KKT conditions to be applicable only when the number of constraints involved is at the most equal to the number of decision variables (without loss of generality) I am just learning this concept and I got stuck in this question. Any suggestions would be helpful.

EDIT I tried to introduce a fourth variable $$x_4$$ with zero coefficient everywhere. \begin{align} & 2x_1 = -2u_1 - u_2 +u_3\\ & 2x_2 =-u_1 - u_4\\ & 2x_3=-u_2\\ & u_1(5-2x_1-x_2)=0\\ & u_2(2-x_1-x_3)=0\\ & u_3(x_1-1)=0\\ & u_4(x_2-2)=0\\ & 2-x_1-x_3 \geq 0\\ & 5-2x_1-x_2 \geq 0\\ & x_1 - 1 \geq 0\\ & x_2-2 \geq 0 \end{align} This looks bulky to proceed with. Is this correct and what should I do next?

• Also just in case if translation of the required axes would help in this question to reduce the number of constraints?
– S.S
Commented Jun 14, 2019 at 20:13
• It is rather difficult to read the problem. Commented Jun 14, 2019 at 20:26
• @copper.hat Is it better now?
– S.S
Commented Jun 14, 2019 at 20:51
• What if you just added a new variable $x_4$ that has a coefficient of 0 in the objective function and every constraint? Commented Jun 15, 2019 at 1:36
• I bet you That's one of those "without lost of generality" assumptions, read your text book again and carefully. Commented Jun 15, 2019 at 4:57

$$\max_X f(X) \ \ \text{s. t.}\ \ g(X) \ge 0$$

introducing slack variables to transform the inequalities into equations and calling

$$f(x,y,z)=-(x^2+y^2+z^2)\\ g_1(x,y,z,s)=5-2x-y-s^2\\ g_2(x,y,z,s)=2-x-z-s^2\\ g_3(x,y,z,s)=x-1-s^2\\ g_4(x,y,z,s)=y-2-s^2\\ g_5(x,y,z,s)=z-s^2$$

we have the lagrangian

$$L(X,\lambda,S) = f(X)+\sum_k \lambda_k g_k(X,s_k)$$

so the stationary points are given for the solutions to

$$\nabla L = 0 = \left\{ \begin{array}{rcl} -2 \lambda_1-\lambda_2+\lambda_3-2 x=0 \\ -\lambda_1+\lambda_4-2 y=0 \\ -\lambda_2+\lambda_5-2 z=0 \\ -2 \lambda_1 s_1=0 \\ -2 \lambda_2 s_2=0 \\ -2 \lambda_3 s_3=0 \\ -2 \lambda_4 s_4=0 \\ -2 \lambda_5 s_5=0 \\ 5-2 x-y=s_1^2 \\ 2-x-z=s_2^2 \\ x-1=s_3^2 \\ y-2=s_4^2 \\ z=s_5^2 \\ \end{array} \right.$$

Solving this system of equations we get at

$$\left[ \begin{array}{cccccccccccccc} f & x & y & z & \lambda_1 & \lambda_2 &\lambda_3 &\lambda_4&\lambda_5&s_1^2&s_2^2&s_3^2&s_4^2&s_5^2\\ -11 & 1 & 3 & 1 & -6 & -2 & -12 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\ -10 & 1 & 3 & 0 & -6 & 0 & -10 & 0 & 0 & 0 & 1 & 0 & 1 & 0 \\ -\frac{13}{2} & \frac{3}{2} & 2 & \frac{1}{2} & -1 & -1 & 0 & 3 & 0 & 0 & 0 & \frac{1}{2} & 0 & \frac{1}{2} \\ -\frac{25}{4} & \frac{3}{2} & 2 & 0 & -\frac{3}{2} & 0 & 0 & \frac{5}{2} & 0 & 0 & \frac{1}{2} & \frac{1}{2} & 0 & 0 \\ -6 & 1 & 2 & 1 & 0 & -2 & 0 & 4 & 0 & 1 & 0 & 0 & 0 & 1 \\ -5 & 1 & 2 & 0 & 0 & 0 & 2 & 4 & 0 & 1 & 1 & 0 & 0 & 0 \\ \\\end{array} \right]$$

Here the $$s_k=0$$ show the actuating restrictions at each solution.

The final qualification should be done using the KKT criteria. At each solution point $$X_k^*$$ we should have

$$-\nabla f(X_k^*) = \sum_{j\in I_k}\mu_j \nabla g_j(X_k^*)$$

where $$I_k$$ is the set of indices for the active restrictions at $$X_k^*$$, and $$\mu_j\ge 0, j\in I_k$$

Attached a plot showing the feasible region and in blue the stationary points.

NOTE

The solution for the previous equations associated to the stationary points is quite easy to find due to the binary option at $$\lambda_k s_k = 0$$ The solution point is at the bottom left. Black vectors are $$-\nabla f(X^*)$$ and red vectors are $$\nabla g_j(X^*),\, j\in I_k$$

• Is there any easy way to plot those region in computer? Commented Jun 16, 2019 at 18:42
• Just a minor point, I think you need to discus that a constraint qualification holds after adding nonlinear slack variables. otherwise there's no guarantee the solution you get from KKT system is optimal for above convex optimization. Commented Jun 16, 2019 at 18:50
• @Redshoes The feasible region was plotted with the MATHEMATICA command RegionPlot3D[ 2 x1 + x2 < 5 && x1 + x3 < 2 && 1 < x1 && 2 < x2 && 0 < x3, {x1, 0.5, 1.8}, {x2, 1.4, 3.2}, {x3, -0.3, 1.5}, Mesh -> False, PlotStyle -> Directive[Yellow, Opacity[0.3]], PlotPoints -> 100] Commented Jun 16, 2019 at 19:04