===== Integer Programming and Parameterized Complexity 2019/20 =====

===  First lecture ===

  * What is IP? ILP is NP-hard, but maybe restricted instances can be easier?
  * IP in fixed dimension: flatness theorem, branching, etc.
  * Building intuition using the knapsack problem and its DP. We can use DP to solve knapsack in time $O(n \cdot b)$, and to solve multidimensional knapsack in $d$ dimensions in time $O(n \cdot b^d)$. But what if $b$ is much larger than the item sizes, and/or what if we are given items by multiplicities $\mu_1, \dots, \mu_n$? Turns out we can start from //some// solution and then //iteratively augment// to the optimal solution. Each augmentation requires one DP to be solved, but this DP is simpler than the original one. In the next lecture we will formalize this.

=== Second lecture ===

  * basic notation and assumptions: **[ATIP beginning of sec 2]**
  * showing that the Graver basis $\mathcal{G}(A)$ is always finite. This amounts to the //Gordan-Dickson Lemma//. Proof idea: restrict ourselves to the non-negative orthant. We want to show that the ordering $\leq$ there has no infinite antichain as that will imply that the number of minimal elements is finite. We prove a stronger statement: every infinite set $S \subseteq \mathbb{N}^n$ contains an infinite chain, i.e., elements $a_1 \leq a_2, \leq ...$. Proof goes by induction over $n$ (the dimension). For $n=1$ it's obvious. Now we assume the statement holds for $n-1$ and want to prove it for $n$. Take $S'$ to be the restriction of $S$ to the first $n-1$ coordinates, apply the IH and obtain a sequence $a_1, a_2,\dots$ (which is a chain when forgetting the $n$-th coordinate). Now two cases can happen. Either there is a number $x \in \mathbb{N}$ which repeats as the last coordinate infinitely many times in the sequence $a_1, a_2, \dots$ -- take all those elements and note that they form a subsequence $b_1, b_2, \dots $ which now forms a chain in all $n$ dimensions. Otherwise, no such number $x$ exists. But that implies that for any $a_i$, there always exists $j > i$ s.t. $(a_j)_n > (a_i)_n$, i.e., we can always "jump up" in the last coordinate.
  * by definition of $\mathcal{G}(A)$, any element from $\ker_{\mathbb{Z}}(A)$ can be decomposed into sign-compatible (conformal) elements of $\mathcal{G}(A)$. However, it is not clear how many (distinct) elements will be needed. A theorem of [[https://www.sciencedirect.com/science/article/pii/009589568690064X|Cook, Fonlupt, and Schrijver]] says that we need at most $2n-1$. The proof is not too hard: considering $\mathcal{G}(A)$ as a matrix with the Graver elements being its columns, we solve the LP $\max 1^{\intercal}y \colon \mathcal{G}(A)y=x, \, y \geq 0$, i.e., we are searching for a (non-integral) decomposition of $x$ into elements of $\mathcal{G}(A)$ and we are //maximizing// (this will be important!) the 1-norm of the decomposition. (We should be more careful here and restrict ourselves to elements of $\mathcal{G}(A)$ which are conformal to $x$.) Assume $y$ is a basic solution, which means that it has at most $n$ non-zero coordinates. Now, take $x' = \mathcal{G}(A) \lfloor y \rfloor$ and observe that $x'' = x-x'$ is an integer vector belonging to $\ker_{\mathbb{Z}}(A)$, so it has some decomposition $\sum_{j} \lambda_j g_j$ into Graver elements. But since the decomposition according to $y$ is of maximum $1$-norm, we have $\sum_j \lambda_j \leq \| y- \lfloor y \rfloor \|_1 < n$. Hence we found a decomposition of $x'$ into at most $n$ elements and $x''$ into at most $n-1$ elements, altogether $2n-1$ elements. (Feel free to look into the original paper, the proof is fairly intelligible.)
  * more definitions from **[ATIP 2.1]** such as feasible solution, augmenting step, Graver-best step, halfling, halfling procedure.
  * sketched proof that halfling procedure converges quickly. I'm only interested in the fact that it requires $O(n \log f_{\max})$ iterations, don't bother with the constants. (The calculations get a bit complicated.)
  * proof that a step which is "best" for step-lengths 1, 2, 4,... is a halfling.


==== Materials ====

  * The primary material is my paper [[https://arxiv.org/pdf/1904.01361.pdf|An Algorithmic Theory of Integer Programming]], but I will try to make the exposition here much gentler. (The paper is already long, so it tries to take a "shortest path" approach to laying the basics.) In the lecture notes I will refer to it as **[ATIP]**.
  * The fixed dimension part was inspired by the exposition of Fritz Eisenbrand in [[http://www.bioinfo.org.cn/~dbu/AlgorithmCourses/Lectures/50YearsIP.pdf|50 Years of Integer Programming]], pg. 515 in the linked PDF. In particular look at Section 14.8, i.e., pages 535-537. But I only expect an understanding of the basic idea of Lenstra's algorithm.