--- title: Biostat 216 Homework 4 subtitle: 'Due Oct 30 @ 11:59pm' format: html: theme: cosmo embed-resources: true number-sections: false toc: true toc-depth: 4 toc-location: left code-fold: false jupyter: jupytext: formats: 'ipynb,qmd' text_representation: extension: .qmd format_name: quarto format_version: '1.0' jupytext_version: 1.15.2 kernelspec: display_name: Julia 1.10.5 language: julia name: julia-1.10 --- Submit a PDF (scanned/photographed from handwritten solutions, or converted from RMarkdown or Jupyter Notebook or Quarto) to Gradescope in BruinLearn. ## Q1. Show the following claims. 1. If $\mathcal{S}_1$ and $\mathcal{S}_2$ are two vector spaces of same order, then their **intersection** $\mathcal{S}_1 \cap \mathcal{S}_2$ is a vector space. 2. If $\mathcal{S}_1$ and $\mathcal{S}_2$ are two vector spaces of same order, then their **union** $\mathcal{S}_1 \cup \mathcal{S}_2$ is not necessarily a vector space. 3. The **span** of a set of $\mathbf{x}_1,\ldots,\mathbf{x}_k \in \mathbb{R}^n$, defined as the set of all possible linear combinations of $\mathbf{x}_i$s $$ \text{span} \{\mathbf{x}_1,\ldots,\mathbf{x}_k\} = \left\{\sum_{i=1}^k \alpha_i \mathbf{x}_i: \alpha_i \in \mathbb{R} \right\}, $$ is a vector space in $\mathbb{R}^n$. 4. The null space of an matrix $\mathbf{A} \in \mathbb{R}^{m \times n}$ is a vector space. ## Q2. Let $$ \mathbf{A}_1 = \begin{pmatrix} 1 & 3 & -2 \\ 3 & 9 & -6 \\ 2 & 6 & -4 \end{pmatrix}, \quad \mathbf{A}_2 = \begin{pmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9 \end{pmatrix}. $$ 1. Find the matrices $\mathbf{C}_1$ and $\mathbf{C}_2$ containing independent columns of $\mathbf{A}_1$ and $\mathbf{A}_2$. 2. Find a rank factorization $\mathbf{A} = \mathbf{C} \mathbf{R}$ of each of $\mathbf{A}_1$ and $\mathbf{A}_2$. 3. Produce a basis for the column spaces of $\mathbf{A}_1$ and $\mathbf{A}_2$. What are the dimensions of those column spaces (the number of independent vectors)? What are the ranks of $\mathbf{A}_1$ and $\mathbf{A}_2$? How many independent rows in $\mathbf{A}_1$ and $\mathbf{A}_2$? ## Q3. 1. Show that an **orthocomplement set** of a set $\mathcal{X}$ (not necessarily a subspace) in a vector space $\mathcal{V} \subseteq \mathbb{R}^m$ $$ \mathcal{X}^\perp = \{ \mathbf{u} \in \mathcal{V}: \langle \mathbf{x}, \mathbf{u} \rangle = 0 \text{ for all } \mathbf{x} \in \mathcal{X}\} $$ is a vector space. 2. Show that, if $\mathcal{S}$ is a subspace of a vector space $\mathcal{V} \subseteq \mathbb{R}^m$, then $\mathcal{S} = (\mathcal{S}^\perp)^\perp$. ## Q4. In this exercise, we show the fact $$ \text{rank}(\mathbf{A} + \mathbf{B}) \le \text{rank}(\mathbf{A}) + \text{rank}(\mathbf{B}) $$ for any two matrices $\mathbf{A}$ and $\mathbf{B}$ of same size in steps. 1. Show that the sum of two vector spaces $\mathcal{S}_1$ and $\mathcal{S}_2$ of same order $$ \mathcal{S}_1 + \mathcal{S}_2 = \{\mathbf{x}_1 + \mathbf{x}_2: \mathbf{x}_1 \in \mathcal{S}_1, \mathbf{x}_2 \in \mathcal{S}_2\} $$ is a vector space. 2. Show that $\text{dim}(\mathcal{S}_1 + \mathcal{S}_2) \le \text{dim}(\mathcal{S}_1) + \text{dim}(\mathcal{S}_2)$. 3. Show that $\mathcal{C}(\mathbf{A} + \mathbf{B}) \subseteq \mathcal{C}(\mathbf{A}) + \mathcal{C}(\mathbf{B})$. 4. Conclude that $\text{rank}(\mathbf{A} + \mathbf{B}) \le \text{rank}(\mathbf{A}) + \text{rank}(\mathbf{B})$. ## Q5. Fundamental theorem of ranks 1. Show that $\text{rank}(\mathbf{A}) = \text{rank}(\mathbf{A}'\mathbf{A})$ and $\text{rank}(\mathbf{A}') = \text{rank}(\mathbf{A}\mathbf{A}')$. Hint: we did it in class, using rank-nullity theorem. 2. Show that $\text{rank}(\mathbf{A}) = \text{rank}(\mathbf{A}')$ using part 1. Hint: we showed this remarkable result using rank factorization in class. This question asks you to show it using part 1. ## Q6. 1. If $\mathbf{A}$ and $\mathbf{B}$ are two matrices with the same number of rows, then $$ \mathcal{C}(\begin{pmatrix} \mathbf{A} \,\,\, \mathbf{B} \end{pmatrix}) = \mathcal{C}(\mathbf{A}) + \mathcal{C}(\mathbf{B}). $$ 2. If $\mathbf{A}$ and $\mathbf{C}$ are two matrices with the same number of columns, then $$ \mathcal{R} \left( \begin{pmatrix} \mathbf{A} \\ \mathbf{C} \end{pmatrix} \right) = \mathcal{R}( \mathbf{A} ) + \mathcal{R}( \mathbf{C} ) $$ and $$ \mathcal{N} \left( \begin{pmatrix} \mathbf{A} \\ \mathbf{C} \end{pmatrix} \right) = \mathcal{N}( \mathbf{A} ) \cap \mathcal{N}( \mathbf{C} ). $$