The statement above is true.

Let the following information hold:

• c is a scalar.
• c\vec{w} is the projection of \vec{u} onto \vec{w}.

When a vector is orthogonal to another their dot product will equal zero. This means \vec{v} \cdot \vec{w} = 0.

From lecture we know: c\vec{w} = \frac{\vec{w} \cdot \vec{u}}{\vec{w} \cdot \vec{w}} \cdot \vec{w}

So, now we can solve c\vec{w} \cdot \vec{v} = \frac{\vec{w} \cdot \vec{u} \cdot \vec{w} \cdot \vec{v}}{\vec{w} \cdot \vec{w}}. The numerator becomes zero because \vec{v} \cdot \vec{w} = 0 and anything multiplied by zero will also be zero. This means the projection of \vec{u} onto \vec{w} is orthogonal to \vec v.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 52%.

The statement above is false.

Let the following hold:

• \vec v = \begin{bmatrix} 1 \\ 2 \end{bmatrix}
• \vec w = \begin{bmatrix} -2 \\ 1 \end{bmatrix}
• \vec u = \begin{bmatrix} 0 \\ 1 \end{bmatrix}

Let’s check if \vec w \cdot \vec v = 0. (1)(-2)+(1)(2) = 0 That works! Now we can calculate the projection of \vec w onto \vec u.

\begin{align*} \frac{\vec w \cdot \vec u}{\vec u \cdot \vec u}\vec u &= \frac{(-2)(0)+(1)(1)}{(0)(0)+(1)(1)} \cdot \begin{bmatrix} 0 \\ 1 \end{bmatrix}\\ &= -1 \cdot \begin{bmatrix} 0 \\ 1 \end{bmatrix}\\ &= \begin{bmatrix} 0 \\ -1 \end{bmatrix} \end{align*}

Now we can check to see if \vec v \cdot the projection of \vec w onto \vec u is equal to zero.

\begin{align*} &\begin{bmatrix} 0 \\ -1 \end{bmatrix} \cdot \begin{bmatrix} 1 \\ 2 \end{bmatrix}\\ &= (0)(1) + (2)(-1) = -1\\ &\text{and } -1 \neq 0 \end{align*}

We can see that \vec v \cdot the projection of \vec w onto \vec u is not equal to zero.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 50%.

An m-dimensional vector.

We know the following information:

• M is an m \times n matrix.
• \vec{v} is an n-dimensional vector.

This means:

M = \begin{bmatrix} & & \\ & & \\ & & \\ \end{bmatrix}_{m \times n} \text{ and } \vec{v} = \begin{bmatrix} & \\ & \\ \end{bmatrix}_{n \times 1}

When you multiply M \vec{v} the n will both cancel out leaving you with an object with the size \begin{bmatrix}& \\& \\& \\\end{bmatrix}_{m \times 1}, which is an m-dimensional vector.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

An n \times n matrix.

We know the following information:

• M is an m \times n matrix.

This means:

M^T = \begin{bmatrix} & & & \\ & & & \\ \end{bmatrix}_{n \times m} \text{ and } M = \begin{bmatrix} & & \\ & & \\ & & \\ \end{bmatrix}_{m \times n}

When you multiply M^T M the m will both cancel out leaving you with an object with the size \begin{bmatrix}& & \\& & \\\end{bmatrix}_{n \times n}, which is an n \times n matrix.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

A scalar.

We know the following information:

• \vec{v} is an n-dimensional vector.
• N is an n \times n matrix.

This means:

\vec{v}^T = \begin{bmatrix} & & \end{bmatrix}_{1 \times n} \text{, } N = \begin{bmatrix} & & \\ & & \\ \end{bmatrix}_{n \times n} \text{, and } \vec{v} = \begin{bmatrix} & \\ & \\ \end{bmatrix}_{n \times 1}

When you multiply \vec{v}^T N the n will both cancel out leaving you with an object with the size \begin{bmatrix}& &\end{bmatrix}_{1 \times n}. When you multiply this object by \vec{v} the n will cancel out again leaving you with an object with the size \begin{bmatrix}&\end{bmatrix}_{1 \times 1}, which is a scalar.

---

Difficulty: ⭐️

The average score on this problem was 93%.

An n-dimensional vector.

We know the following information:

• \vec{v} is an n-dimensional vector.
• N is an n \times n matrix.
• s is a scalar.

This means:

\vec{v} = \begin{bmatrix} & \\ & \\ \end{bmatrix}_{n \times 1} \text{, } N = \begin{bmatrix} & & \\ & & \\ \end{bmatrix}_{n \times n} \text{, and } s = \begin{bmatrix} & \end{bmatrix}_{1 \times 1}

When you multiply N \vec{v} the n will both cancel out leaving you with an object with the size \begin{bmatrix}& \\& \\\end{bmatrix}_{n \times 1}. When you multiply s \vec{v} the dimensions of \vec{v} does not change, so you will have an object with the size \begin{bmatrix}& \\& \\\end{bmatrix}_{n \times 1}. When you add these vectors the dimension will not change, so you are left with an n-dimensional vector.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

An n-dimensional vector.

We know the following information:

• \vec{v} is an n-dimensional vector.
• M is an m \times n matrix.
• M^T is an n \times m matrix.

This means:

M^T = \begin{bmatrix} & & & \\ & & & \\ \end{bmatrix}_{n \times m} \text{, } M = \begin{bmatrix} & & \\ & & \\ & & \\ \end{bmatrix}_{m \times n} \text{, and } \vec{v} = \begin{bmatrix} & \\ & \\ \end{bmatrix}_{n \times 1}

When you multiply M^T M the m will both cancel out leaving you with an object with the size \begin{bmatrix}& & \\& & \\\end{bmatrix}_{n \times n}, which is an n \times n matrix.

When you multiply \begin{bmatrix}& & \\& & \\\end{bmatrix}_{n \times n} \times \begin{bmatrix}& \\&\\\end{bmatrix}_{n \times 1} the n will cancel out leaving you with an n-dimensional vector.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

To begin with, we know from the bullet points above that A^T \vec e = 0 in order for A^T A \vec{b} = A^T \vec{d} to be true. So we start by interpreting A^T \vec e = 0 in \vec e’s other form \vec{d} - \vec{c}. We get A^T(\vec d - \vec c)!

We then distribute A^T to get A^T \vec d - A^T \vec c = 0. From here we can move A^T \vec c to the right to get A^T \vec d = A^T \vec c.

From the third bullet point we find \vec c = A\vec{b}, so we can write A^T \vec d = A^T A \vec b. Thus we have proved if \vec{e} is orthogonal to every column in A that \vec{b} must satisfy the following equation: A^T A \vec{b} = A^T \vec{d}.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 58%.

Linearly Independent

Vectors \vec{a} and \vec{b} are linearly independent because they aren’t multiples of each other. They each point in different (but not opposite) directions, so we can’t express either vector as a scalar multiple of the other.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

Plane

Any two linearly independent vectors must span a plane. Geometrically, we can think of all scalar multiples of a single vector to lie along a single line. Then, linear combinations of any two vectors pointing in different directions must lie on the same plane.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

is

A quick way to know that \vec{c} lies in the span of \vec{a} and \vec{b} is because we are dealing with 2-dimensional vectors and we know that \vec{a} and \vec{b} span a plane (so they must span all of \mathbb{R}^2). \vec{c} exists in 2-dimensional space, so it must exist in the span of \vec{a} and \vec{b}.

We can also directly check if \vec{c} lies in the span of \vec{a} and \vec{b} if there exists some constants c_1 and c_2 such that c_1 \vec{a} + c_2 \vec{b} = \vec{c}. Plugging in what we know: c_1 \begin{bmatrix} 1 \\ 3 \end{bmatrix} + c_2 \begin{bmatrix} -2 \\ 1 \end{bmatrix} = \begin{bmatrix} -6 \\ 11 \end{bmatrix} This gives us the system of equations: c_1 (1) + c_2 (-2) = -6 c_1 (3) + c_2 (1) = 11 Solving this system gives us c_1 = \frac{16}{7}, c_2 = \frac{29}{7}, which means \vec{c} does lie in the span of \vec{a} and \vec{b}.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 68%.

neither orthogonal nor collinear

The vectors \vec{a} and \vec{b} are orthogonal if \vec{a} \cdot \vec{b} = 0. We can compute \vec{a} \cdot \vec{b} = (1)(-2) + (3)(1) = 1 \neq 0, so \vec{a} and \vec{b} are not orthogonal.

The vectors \vec{a} and \vec{b} are collinear if we can write one vector as a scalar multiple of the other. But we already know \vec{a} and \vec{b} are linearly independent, so they don’t lie along the same line and aren’t collinear.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 68%.

something else

If two vectors are orthogonal, the angle between them is 90 degrees. If two vectors are collinear, the angle between them is 0 or 180 degrees. Since \vec{a} and \vec{b} are neither of these, then the angle between them must be something else.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

Linearly Dependent

To check if \vec{a}, \vec{b}, \vec{c} are linearly independent or dependent, it is possible to try a few different linear combinations of two vectors, and see if it equals the third vector. In this case, we can see that 4\vec{a} + 2\vec{b} = \vec{c}, so they are linearly dependent. For a more straight-forward computation, we can arrange these vectors as the columns of a matrix and compute their rank: A = \begin{bmatrix} -2 & 1 & -6 \\ 1.5 & 2 & 10 \\ 4 & -2.5 & 11\end{bmatrix} Then, we can perform row operations to reduce the matrix A. This gives us RREF(A) = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 0\end{bmatrix} From here, we see that there only two columns with leading 1s, which implies the vectors are linearly dependent.

---

Difficulty: ⭐️

The average score on this problem was 94%.

Plane

We know from above that the three vectors are linearly dependent. It is clear that they all do not lie on the same line, since we can’t multiply any single vector by a constant to obtain another vector. If all three vectors were independent, they would span all of \mathbb{R}^3, or 3-dimensional space. Since this isn’t the case, they must span a plane.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 63%.

is not

If \vec{d} was in the span of \vec{a}, \vec{b}, \vec{c}, then there should exist constants to fulfill the equation: c_1 \vec{a} + c_2 \vec{b} + c_3 \vec{c} = \vec{d} We can set this up as an augmented matrix: A = \begin{bmatrix} -2 & 1 & -6 & -2 \\ 1.5 & 2 & 10 & 15 \\ 4 & -2.5 & 11 & -7 \end{bmatrix} After performing row operations, we obtain: RREF(A) = \begin{bmatrix} 1 & 0 & 4 & 0 \\ 0 & 1 & 2 & 0 \\ 0 & 0 & 0 & 1\end{bmatrix} This tells us our system of equations is inconsistent because the last row implies 0=1. Therefore, there does not exist a solution, and \vec{d} is not in the span of \vec{a}, \vec{b}, \vec{c}.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 50%.

neither orthogonal nor collinear

We can tell that \vec{a}, \vec{b}, \vec{c} are not collinear because their span isn’t a line. In order to check if these vectors are orthogonal, we can compute the dot product between each pair of vectors; if any of the three dot products are not equal to 0, then they are not orthogonal.

We see that \vec{a} \cdot \vec{b} = (-2)(1) + (1.5)(2) + (4)(-2.5) = -9 \neq 0, so the three vectors together are not orthogonal.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

something else

If two vectors are orthogonal, the angle between them is 90 degrees. If two vectors are collinear, the angle between them is 0 or 180 degrees. Since each pair of the vectors \vec{a}, \vec{b}, \vec{c} are neither of these, then the angles between them must be something else.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

Linearly Dependent

Upon inspection, we can see that \vec{b} = -\frac{1}{2}\vec{a}. Therefore, these vectors are linearly dependent.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

Line

Since we found that \vec{a} and \vec{b} are linearly dependent, they must span a space of only 1-dimension. This means that \vec{a} and \vec{b} lie the same line.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

is not

We know that \vec{a} and \vec{b} span a line consisting only of vectors that are scalar multiples of \vec{a} or \vec{b}. We can see that the first component of \vec{c} is three times larger than the first component of \vec{a}, but they both share the same second component. This means it is not possible to scale \vec{a} by a constant to obtain \vec{c}, and so \vec{c} does not lie on the same line spanned by \vec{a} and \vec{b}.

---

Difficulty: ⭐️

The average score on this problem was 93%.

Collinear

The vectors \vec{a} and \vec{b} are collinear if we can write one vector as a scalar multiple of the other. We saw that \vec{b} = -\frac{1}{2}\vec{a}, so they are collinear.

---

Difficulty: ⭐️⭐️⭐️

The average score on this problem was 62%.

0 or 180 degrees

If two vectors are collinear, the angle between them is 0 or 180 degrees. This is because they lie on the same line and either point in the same or opposite directions from the origin.

---

Difficulty: ⭐️⭐️

The average score on this problem was 81%.

Linearly Independent

We can see that one vector is not a scalar multiple of the other. \vec{a} has both negative components, so we would only be able to obtain scalar multiples with both negative or both positive components. On the other hand, \vec{b} has one negative and one positive component. So they must be linearly independent.

---

Difficulty: ⭐️⭐️

The average score on this problem was 75%.

Plane

Any two linearly independent vectors must span a plane. Geometrically, we can think of all scalar multiples of a single vector to lie along a single line. Then, linear combinations of any two vectors pointing in different directions must lie on the same plane.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

is

Similar to Q5, we can tell that \vec{c} lies in the span of \vec{a} and \vec{b} because we are dealing with 2-dimensional vectors, and we know that \vec{a} and \vec{b} span a plane (so they must span all of \mathbb{R}^2). \vec{c} exists in 2-dimensional space, so it must exist in the span of \vec{a} and \vec{b}.

We can also directly check if \vec{c} lies in the span of \vec{a} and \vec{b} if there exists some constants c_1 and c_2 such that c_1 \vec{a} + c_2 \vec{b} = \vec{c}. Plugging in what we know: c_1 \begin{bmatrix} -3 \\ -2 \end{bmatrix} + c_2 \begin{bmatrix} -2 \\ 3 \end{bmatrix} = \begin{bmatrix} 1 \\ 1 \end{bmatrix}$ This gives us the system of equations: c_1 (-3) + c_2 (-2) = 1 c_1 (-2) + c_2 (3) = 1 Solving this system gives us c_1 = -\frac{5}{13}, c_2 = \frac{1}{13}, which means \vec{c} does lie in the span of \vec{a} and \vec{b}.

---

Difficulty: ⭐️⭐️

The average score on this problem was 75%.

Orthogonal

The vectors \vec{a} and \vec{b} are orthogonal if \vec{a} \cdot \vec{b} = 0. We can compute \vec{a} \cdot \vec{b} = (-3)(-2) + (-2)(3) = 6 - 6 = 0, so \vec{a} and \vec{b} are orthogonal.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

90 degrees

Since \vec{a} and \vec{b} are orthogonal, the angle between them is 90 degrees.

---

Difficulty: ⭐️⭐️

The average score on this problem was 87%.

252.

Vectors in \text{span}(\vec u, \vec v) must have an equal 2nd and 3rd component, and the third component is 252, so the second must be as well.

We can construct the following series of matrices to get (X^TX)^{-1}X^T.

• X = \begin{bmatrix} 1 & 0 \\ 0 & 1 \\ 0 & 1 \end{bmatrix}
• X^T = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 1 \end{bmatrix}
• X^TX = \begin{bmatrix} 1 & 0 \\ 0 & 2 \end{bmatrix}
• (X^TX)^{-1} = \begin{bmatrix} 1 & 0 \\ 0 & \frac{1}{2} \end{bmatrix}
• (X^TX)^{-1}X^T = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \frac{1}{2} & \frac{1}{2} \end{bmatrix}

a = 4, b = 5.

The result from the part (b) implies that when using the normal equations to find coefficients for \vec u and \vec v – which we know from lecture produce an error vector whose length is minimized – the coefficient on \vec u must be y_1 and the coefficient on \vec v must be \frac{y_2 + y_3}{2}. This can be shown by taking the result from part (b), \begin{bmatrix} 1 & 0 & 0 \\ 0 & \frac{1}{2} & \frac{1}{2} \end{bmatrix}, and multiplying it by the vector \vec y = \begin{bmatrix} y_1 \\ y_2 \\ y_3 \end{bmatrix}.

Here, y_1 = 4, so a = 4. We also know y_2 = 2 and y_3 = 8, so b = \frac{2+8}{2} = 5.

3 \sqrt{2}.

The correct value of a \vec u + b \vec v = \begin{bmatrix} 4 \\ 5 \\ 5\end{bmatrix}. Then, \vec{e} = \begin{bmatrix} 4 \\ 2 \\ 8 \end{bmatrix} - \begin{bmatrix} 4 \\ 5 \\ 5 \end{bmatrix} = \begin{bmatrix} 0 \\ -3 \\ 3 \end{bmatrix}, which has a length of \sqrt{0^2 + (-3)^2 + 3^2} = 3\sqrt{2}.

Yes, but for a reason that isn’t listed here.

Here’s the full reason: 1. We can use the normal equations to find c and d, no matter what \vec{y} is. 2. The error vector \vec e that results from using the normal equations is such that \vec e is orthogonal to the span of the columns of X. 3. The columns of X are just \vec u and \vec v. So, \vec e is orthogonal to any linear combination of \vec u and \vec v. 4. One of the many linear combinations of \vec u and \vec v is \begin{bmatrix} 1 \\ 0 \\ 0 \end{bmatrix} + \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix} = \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix}. 5. This means that the vector \vec e is orthogonal to \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix}, which means that \vec{1}^T \vec{e} = 0 \implies \sum_{i = 1}^3 e_i = 0.

Correct: Scalar.

• \vec{s}^T: 1 x 100
• Q: 100 x 12.
• \vec{f}: 12 x 1.

The inner dimensions of 100 and 12 cancel, and so \vec{s}^T Q \vec{f} is of shape 1 x 1.

30.

The minimizer of empirical risk for the constant model when using zero-one loss is the mode.

7.

The minimizer of empirical risk for the constant model when using absolute loss is the median. If the bar at 30 wasn’t there, the median would be 6, but the existence of that bar drags the “halfway” point up slightly, to 7.

11.

The minimizer of empirical risk for the constant model when using squared loss is the mean. The mean is heavily influenced by the presence of outliers, of which there are many at 30, dragging the mean up to 11. While you can’t calculate the mean here, given the large right tail, this question can be answered by understanding that the mean must be larger than the median, which is 7, and 11 is the next biggest option.

15.

The minimizer of empirical risk for the constant model when using infinity loss is the midrange, i.e. halfway between the min and max.

The values of the three blanks are: y_2, z_3, and any value between z_1 and z_2 (inclusive).

For the first blank, we know the median of the y-dataset minimizes mean absolute error of a constant prediction on the y-dataset. Since y_1 < y_2 < y_3, y_2 is the unique minimizer.

For the second blank, we can also use the fact that the median of the z-dataset minimizes mean absolute error of a constant prediction on the z-dataset. Since z_1 < z_2 < z_3 < z_4 < z_5, z_3 is the unique minimizer.

For the third blank, we know that when there are an odd number of data points in a dataset, any values between the middle two (inclusive) minimize mean absolute error. Here, the middle two in the full dataset of 8 are z_1 and z_2.

\alpha = \frac{3}{8}, \beta = \frac{5}{8}.

To find \alpha and \beta, we need to construct a similar-looking equation to the one above. We can start by looking at our equation for T_\text{abs}(h):

T_\text{abs}(h) = \frac{1}{8} \sum_{i = 1}^8 |t_i - h|

Now, we can split the sum on the right hand side into two sums, one for our y data points and one for our z data points:

T_\text{abs}(h) = \frac{1}{8} \left(\sum_{i = 1}^3 |y_i - h| + \sum_{i = 1}^5 |z_i - h|\right)

Each of these two mini sums can be represented in terms of Y_\text{abs}(h) and Z_\text{abs}(h):

T_\text{abs}(h) = \frac{1}{8} \left(3 \cdot Y_\text{abs}(h) + 5 \cdot Z_\text{abs}(h)\right) T_\text{abs}(h) = \frac38 \cdot Y_\text{abs}(h) + \frac58 \cdot Z_\text{abs}(h)

By looking at this final equation we’ve built, it is clear that \alpha = \frac{3}{8} and \beta = \frac{5}{8}.

We can start by plugging z_1 into Y_\text{abs}(h):

Y_\text{abs}(z_1) = \frac{1}{3} \left( | z - y_1 | + | z - y_2 | + |z - y_3| \right)

Since z_1 > y_3 > y_2 > y_1, all of the absolute values can be dropped and we can write:

Y_\text{abs}(z_1) = \frac{1}{3} \left( (z_1 - y_1) + (z_1 - y_2) + (z_3 - y_3) \right)

This can be simplified to:

Y_\text{abs}(h) = \frac{1}{3} \left( 3z_1 - (y_1 + y_2 + y_3) \right) = \frac{1}{3} \left( 3z_1 - 3 \cdot \bar{y}\right) = z_1 - \bar{y} = z_1 - 2.

3.

We are given that T_\text{abs}(\text{median}) = 6. This means that any value (inclusive) between z_1 and z_2 minimize T_\text{abs}(h), with the output being always being 6. So, T_\text{abs}(z_1) = 6. Let’s see what happens when we plug this into T_\text{abs}(h) = \frac{3}{8} Y_\text{abs}(h) + \frac{5}{8} Z_\text{abs}(h) from earlier:

T_\text{abs}(z_1) = \frac{3}{8} Y_\text{abs}(z_1) + \frac{5}{8} Z_\text{abs}(z_1) (6) = \frac{3}{8} Y_\text{abs}(z_1) + \frac{5}{8} Z_\text{abs}(z_1)

If we could write Y_\text{abs}(z_1) and Z_\text{abs}(z_1) in terms of z_1, we could solve for z_1 and our work would be done, so let’s do that. Y_\text{abs}(z_1) = z_1 - 2 from earlier. Z_\text{abs}(z_1) simplifies as follows (knowing that \bar{z} = 12):

Z_\text{abs}(z_1) = \frac{1}{5} \sum_{i = 1}^5 |z_i - z_1| Z_\text{abs}(z_1) = \frac{1}{5} \left(|z_1 - z_1| + |z_2 - z_1| + |z_3 - z_1| + |z_4 - z_1| + |z_5 - z_1|\right) Z_\text{abs}(z_1) = \frac{1}{5} \left(0 + (z_2 + z_3 + z_4 + z_5) - 4z_1\right) Z_\text{abs}(z_1) = \frac{1}{5} \left((z_2 + z_3 + z_4 + z_5) - 4z_1 + (z_1 - z_1)\right) Z_\text{abs}(z_1) = \frac{1}{5} \left((z_1 + z_2 + z_3 + z_4 + z_5) - 5z_1\right) Z_\text{abs}(z_1) = \bar{z} - z_1 Z_\text{abs}(z_1) = (12) - z_1

Subbing back into our main equation:

(6) = \frac{3}{8} Y_\text{abs}(z_1) + \frac{5}{8} Z_\text{abs}(z_1) 6 = \frac{3}{8}(z_1 - 2) + \frac{5}{8}(12 - z_1) z_1 = 3