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‎.github/workflows/build.yml

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- name: Set up Python
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uses: actions/setup-python@v5.2.0
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with:
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python-version: '3.8'
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python-version: '3.11'
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- name: Install mkdocs-material
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run: |
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scripts/install-mkdocs.sh

‎mkdocs.yml

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icon:
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repo: fontawesome/brands/github
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features:
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- navigation.tabs
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- toc.integrate
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- search.suggest
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- content.code.copy
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permalink: true
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plugins:
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- toggle-sidebar:
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toggle_button: all
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- mkdocs-simple-hooks:
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hooks:
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on_env: "hooks:on_env"
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- search
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- tags:
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tags_file: tags.md
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- tags
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- literate-nav:
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nav_file: navigation.md
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- git-revision-date-localized:

‎scripts/install-mkdocs.sh

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pip install \
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"mkdocs-material>=9.0.2" \
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mkdocs-toggle-sidebar-plugin \
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mkdocs-macros-plugin \
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mkdocs-literate-nav \
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mkdocs-git-authors-plugin \

‎src/algebra/factorization.md

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Here is a visualization of such a sequence $\{x_i \bmod p\}$ with $n = 2206637$, $p = 317$, $x_0 = 2$ and $f(x) = x^2 + 1$.
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From the form of the sequence you can see very clearly why the algorithm is called Pollard's $\rho$ algorithm.
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<center>![Pollard's rho visualization](pollard_rho.png)</center>
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<div style="text-align: center;">
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<img src="pollard_rho.png" alt="Pollard's rho visualization">
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</div>
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Yet, there is still an open question.
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How can we exploit the properties of the sequence $\{x_i \bmod p\}$ to our advantage without even knowing the number $p$ itself?

‎src/algebra/fibonacci-numbers.md

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$$F_{n-1} F_{n+1} - F_n^2 = (-1)^n$$
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>This can be proved by induction. A one-line proof by Knuth comes from taking the determinant of the 2x2 matrix form below.
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* The "addition" rule:
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$$F_{n+k} = F_k F_{n+1} + F_{k-1} F_n$$
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### Matrix form
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It is easy to prove the following relation:
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To go from $(F_n, F_{n-1})$ to $(F_{n+1}, F_n)$, we can express the linear recurrence as a 2x2 matrix multiplication:
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$$\begin{pmatrix} 1 & 1 \cr 1 & 0 \cr\end{pmatrix} ^ n = \begin{pmatrix} F_{n+1} & F_{n} \cr F_{n} & F_{n-1} \cr\end{pmatrix}$$
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$$
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\begin{pmatrix}
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1 & 1 \\
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1 & 0
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\end{pmatrix}
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\begin{pmatrix}
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F_n \\
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F_{n-1}
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\end{pmatrix}
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=
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\begin{pmatrix}
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F_n + F_{n-1} \\
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F_{n}
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\end{pmatrix}
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=
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\begin{pmatrix}
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F_{n+1} \\
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F_{n}
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\end{pmatrix}
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$$
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This lets us treat iterating the recurrence as repeated matrix multiplication, which has nice properties. In particular,
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$$
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\begin{pmatrix}
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1 & 1 \\
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1 & 0
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\end{pmatrix}^n
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\begin{pmatrix}
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F_1 \\
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F_0
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\end{pmatrix}
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=
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\begin{pmatrix}
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F_{n+1} \\
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F_{n}
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\end{pmatrix}
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$$
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where $F_1 = 1, F_0 = 0$.
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In fact, since
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$$
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\begin{pmatrix} 1 & 1 \\ 1 & 0 \end{pmatrix}
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= \begin{pmatrix} F_2 & F_1 \\ F_1 & F_0 \end{pmatrix}
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$$
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we can use the matrix directly:
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$$
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\begin{pmatrix} 1 & 1 \\ 1 & 0 \end{pmatrix}^n
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= \begin{pmatrix} F_{n+1} & F_n \\ F_n & F_{n-1} \end{pmatrix}
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$$
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Thus, in order to find $F_n$ in $O(log n)$ time, we must raise the matrix to n. (See [Binary exponentiation](binary-exp.md))
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Thus, in order to find $F_n$ in $O(\log n)$ time, we must raise the matrix to n. (See [Binary exponentiation](binary-exp.md))
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```{.cpp file=fibonacci_matrix}
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struct matrix {

‎src/algebra/sieve-of-eratosthenes.md

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In the following image you can see a visualization of the algorithm for computing all prime numbers in the range $[1; 16]$. It can be seen, that quite often we mark numbers as composite multiple times.
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<center>![Sieve of Eratosthenes](sieve_eratosthenes.png)</center>
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<div style="text-align: center;">
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<img src="sieve_eratosthenes.png" alt="Sieve of Eratosthenes">
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</div>
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The idea behind is this:
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A number is prime, if none of the smaller prime numbers divides it.

‎src/combinatorics/burnside.md

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return sum / s.size();
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}
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```
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## Practice Problems
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* [CSES - Counting Necklaces](https://cses.fi/problemset/task/2209)
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* [CSES - Counting Grids](https://cses.fi/problemset/task/2210)
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* [Codeforces - Buildings](https://codeforces.com/gym/101873/problem/B)
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* [CS Academy - Cube Coloring](https://csacademy.com/contest/beta-round-8/task/cube-coloring/)

‎src/data_structures/fenwick.md

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The following image shows a possible interpretation of the Fenwick tree as tree.
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The nodes of the tree show the ranges they cover.
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<center>![Binary Indexed Tree](binary_indexed_tree.png)</center>
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<div style="text-align: center;">
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<img src="binary_indexed_tree.png" alt="Binary Indexed Tree">
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</div>
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## Implementation
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‎src/dynamic_programming/divide-and-conquer-dp.md

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- [SPOJ - LARMY](https://www.spoj.com/problems/LARMY/)
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- [SPOJ - NKLEAVES](https://www.spoj.com/problems/NKLEAVES/)
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- [Timus - Bicolored Horses](https://acm.timus.ru/problem.aspx?space=1&num=1167)
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- [USACO - Circular Barn](http://www.usaco.org/index.php?page=viewproblem2&cpid=616)
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- [USACO - Circular Barn](https://usaco.org/index.php?page=viewproblem2&cpid=626)
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- [UVA - Arranging Heaps](https://onlinejudge.org/external/125/12524.pdf)
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- [UVA - Naming Babies](https://onlinejudge.org/external/125/12594.pdf)
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‎src/dynamic_programming/knuth-optimization.md

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\sum\limits_{i=1}^N \sum\limits_{j=i}^{N-1} [opt(i+1,j+1)-opt(i,j)].
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$$
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As you see, most of the terms in this expression cancel each other out, except for positive terms with $j=N$ and negative terms with $i=1$. Thus, the whole sum can be estimated as
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As you see, most of the terms in this expression cancel each other out, except for positive terms with $j=N-1$ and negative terms with $i=1$. Thus, the whole sum can be estimated as
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$$
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\sum\limits_{k=1}^N[opt(k,N)-opt(1,k)] = O(n^2),

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