hw4: done 1

Claudio_Maggioni_4/Claudio_Maggioni_4.md

@@ -0,0 +1,100 @@
+<!-- vim: set ts=2 sw=2 et tw=80: -->
+
+---
+title: Midterm -- Optimization Methods
+author: Claudio Maggioni
+header-includes:
+- \usepackage{amsmath}
+- \usepackage{hyperref}
+- \usepackage[utf8]{inputenc}
+- \usepackage[margin=2.5cm]{geometry}
+- \usepackage[ruled,vlined]{algorithm2e}
+- \usepackage{float}
+- \floatplacement{figure}{H}
+- \hypersetup{colorlinks=true,linkcolor=blue}
+
+---
+\maketitle
+
+# Exercise 1
+
+## Exercise 1.1
+
+The lagrangian is the following:
+
+$$L(X,\lambda) = f(X) - \lambda \left(c(x) - 0\right) = -3x^2 + y^2 + 2x^2 +
+2(x+y+z) - \lambda x^2 - \lambda y^2 -\lambda z^2 + \lambda =$$$$= (-3 -\lambda)x^2 + (1-
+\lambda)y^2 + (2-\lambda)z^2 + 2 (x+y+z) + \lambda$$
+
+The KKT conditions are the following:
+
+First we have the condition on the partial derivatives of the Lagrangian w.r.t.
+$X$:
+
+$$\nabla_X L(X,\lambda) = \begin{bmatrix}(-3-\lambda)x^* + 1\\(1-\lambda)y^* +
+1\\(2-\lambda)z^* + 1\end{bmatrix} = 0 \Leftrightarrow
+\begin{bmatrix}x^*\\y^*\\z^*\end{bmatrix} =
+\begin{bmatrix}\frac1{3+\lambda}\\-\frac1{1-\lambda}\\-\frac{1}{2-\lambda}\end{bmatrix}$$
+
+Then we have the conditions on the equality constraint:
+
+$$c(X) = {x^*}^2 + {y^*}^2 + {z^*}^2 - 1 = 0 \Leftrightarrow \|X^*\| = 1$$
+
+$$\lambda^* c(X^*) = 0 \Leftarrow c(X^*) = 0 \text{ which is true if the above
+condition is true.}$$
+
+Since we have no inequality constraints, we don't need to apply the KKT
+conditions realated to inequality constraints.
+
+## Exercise 1.2
+
+To find feasible solutions to the problem, we apply the KKT conditions. Since we
+have a way to derive $X^*$ from $\lambda^*$ thanks to the first KKT condition,
+we try to find the values of $\lambda$ that satisfies the second KKT condition:
+
+$$c(x) = \left( \frac{1}{3+\lambda} \right)^2 + \left( -\frac{1}{1-\lambda} \right)^2 +
+\left(-\frac{1}{2-\lambda}\right)^2 - 1 =
+\frac{1}{(3+\lambda)^2} + \frac{1}{(1-\lambda)^2} + \frac{1}{(2-\lambda)^2} - 1 =$$$$=
+\frac{(1-\lambda)^2(2-\lambda)^2 + (3+\lambda)^2(2-\lambda)^2 +
+(3+\lambda)^2
+(1-\lambda)^2 - (3+\lambda)^2 (1-\lambda)^2 (2-\lambda)^2}{(3+\lambda)^2
+(1-\lambda)^2 (2-\lambda)^2} = 0
+\Leftrightarrow$$$$\Leftrightarrow
+(1-\lambda)^2(2-\lambda)^2 + (3+\lambda)^2(2-\lambda)^2 +
+(3+\lambda)^2
+(1-\lambda)^2 - (3+\lambda)^2 (1-\lambda)^2 (2-\lambda)^2 = 0
+\Leftrightarrow$$$$\Leftrightarrow
+(\lambda^4 - 6\lambda^3 + 13\lambda^2 - 12\lambda + 16) +
+(\lambda^4 + 2\lambda^3 - 11\lambda^2 - 12\lambda + 36) +
+(\lambda^4 + 4\lambda^3 -  2\lambda^2 - 12\lambda + 9)$$$$
++ (-\lambda^5 -14\lambda^4 +12\lambda^3 +49\lambda^2 -84\lambda + 36) = $$$$
+=-\lambda^5 +17\lambda^4 -12\lambda^3 -49\lambda^2 +48\lambda +13 = 0
+\Leftrightarrow $$$$ \Leftrightarrow
+\lambda = \lambda_1 \approx -0.224 \lor
+\lambda = \lambda_2 \approx -1.892 \lor
+\lambda = \lambda_3 \approx 3.149 \lor
+\lambda = \lambda_4 \approx -4.035$$
+
+We then compute $X$ from each solution and evaluate the objective each time:
+
+$$X = \begin{bmatrix}\frac1{3+\lambda}\\-\frac1{1-\lambda}\\
+-\frac{1}{2-\lambda}\end{bmatrix}
+\Leftrightarrow$$$$\Leftrightarrow
+X = X_1 \approx \begin{bmatrix}0.360\\-0.817\\-0.450\end{bmatrix} \lor
+X = X_2 \approx \begin{bmatrix}0.902\\-0.346\\-0.257\end{bmatrix} \lor
+X = X_3 \approx \begin{bmatrix}0.163\\0.465\\0.870\end{bmatrix} \lor
+X = X_4 \approx \begin{bmatrix}-0.966\\-0.199\\-0.166\end{bmatrix}$$
+
+$$f(X_1) = -1.1304 \;\; f(X_2) = -1.59219 \;\; f(X_3) = 4.64728 \;\; f(X_4) =
+-5.36549$$
+
+We therefore choose $(\lambda_4, X_4)$ since $f(X_4)$ is the smallest objective
+value out of all the feasible points. Therefore, the solution to the
+minimization problem is:
+
+$$X \approx \begin{bmatrix}-0.966\\-0.199\\-0.166\end{bmatrix}$$
+
+
+
+
+

Claudio_Maggioni_4/gsppn.m

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+clc
+
+X = [1/(3+l); (-1)/(1-l); (-1)/(2-l)] 
+
+syms l
+a1 = (3+l)^2;
+a2 = (1-l)^2;
+a3 = (2-l)^2;
+
+t1 = a2*a3
+t2 = a1*a3
+t3 = a1*a2
+t4 = a1*a2*a3
+
+c1 = fliplr(coeffs(t1))
+c2 = fliplr(coeffs(t2))
+c3 = fliplr(coeffs(t3))
+c4 = fliplr(coeffs(t4))
+
+ctot = fliplr(coeffs(t1+t2+t3-t4))
+
+sol = double(solve(t1+t2+t3-t4==0, l, 'Real', true))
+
+for i=1:size(sol, 1)
+    Xi = double(subs(X,l,sol(i)))
+    Li = sol(i);
+    Ci = norm(Xi, 2)^2 - 1;
+    Yi = sum(Xi .* Xi .* [-3;1;2]) + sum(2 * Xi);
+    fprintf("lambda=%.03f ci=%g y=%g", Li, Ci, Yi);
+end
+

Claudio_Maggioni_midterm/Claudio_Maggioni_midterm.md

@@ -281,11 +281,20 @@ since the model quality is good.
 
 ### (f) Does the energy decrease monotonically when Trust Region method is employed? Justify your answer.
 
-In the trust region method the energy of the iterates does not always decrease
+When using the trust region method, the energy of the iterates decreases
-monotonically. This is due to the fact that the algorithm could actively reject
+monotonically. This is true because by construction the algorithm either makes
-a step if the performance measure factor $\rho_k$ is less that a given constant
+the next iterate equal to the current one (i.e. when the performance measure
-$\eta$. In this case, the new iterate is equal to the old one, no step is taken
+$\rho_k$ is too poor to accept a step) or applies a linear, quadratic, or
-and thus the energy norm does not decrease but stays the same.
+blended descending step to the current iterate.
+
+When a step is taken, the step
+by definition should be a solution (or a close approximation of such solution)
+of the energy minimization problem inside the trust region itself. Therefore,
+the step cannot lead to a point that has higher energy than the one from the
+current iterate.
+
+Therefore, the energy either stays constant or decreases at every single
+iteration, and therefore the energy decreases monotonically.
 
 ## Point 2