On March 17, 2020, Dabeen Lee (이다빈) from IBS discrete mathematics group presented a talk on his result proving that the closure of a polyhedron by some generalization of the Chvátal-Gomory cuts gives a polyhedron. The title of his talk is “On a generalization of the Chvátal-Gomory closure“.
Integer programming is the problem of optimizing a linear function over the set of integer solutions satisfying a system of inequalities. The most successful technique in practice is the so-called “cutting-plane” algorithm in combination with branch-and-bound enumeration. Cutting-planes for an integer linear program are linear inequalities that are valid for all integer feasible solutions but cut off intermediate fractional solutions.
The Chvátal-Gomory cuts, introduced by Gomory in 1958 and further studied by Chvátal in 1973 in relation to their applications in combinatorial optimization, are the first class of general-purpose cutting-planes in the literature. The split cuts, whose name was coined by Cook, Kannan, and Schrijver in 1980, are another class of important cutting-planes in modern integer programming. Although there are infinitely many cuts in each class, it is known that only finitely many of them are nonredundant, which is related to designing a finite-convergent cutting-plane algorithm. In this talk, we introduce a new class of cutting-planes that generalizes the Chvátal-Gomory cuts and generalizes a special case of the split cuts. As the two classic classes of cutting-planes, we show that only a finite number of cuts can be redundant.
This talk is based on a joint work with Sanjeeb Dash and Oktay Günlük.
Combinatorial and Discrete Optimization I: November 8, 2019 Friday, 14:20 – 15:40.
Kangbok Lee
Kedong Yan
Dabeen Lee
Se-young Yun
Combinatorial and Discrete Optimization II: November 9, 2019 Saturday, 10:00 – 11:20.
Hyung Chan An
Dong Yeap Kang
Tony Huynh
Sang-il Oum
Abstracts
Kangbok Lee (이강복), Bi-criteria scheduling
The bi-criteria scheduling problems that minimize the two most popular scheduling objectives, namely the makespan and the total completion time, are considered. Given a schedule, makespan, denoted as $C_\max$, is the latest completion time of the jobs and the total completion time, denoted as $\sum C_j$, is the sum of the completion times of the jobs. These two objectives have received a lot of attention in the literature because of their practical implications. Scheduling problems are somehow difficult to solve even for single criterion. On the other hand, when it comes to a bi-criteria problem, a balanced solution coordinating both objectives is indeed essential. In this paper, we consider bi-criteria scheduling problems on $m$ identical parallel machines where $m$ is 2, 3 and an arbitrary number, denoted as $P2 || (C_\max,\sum C_j)$, $P3 || (C_\max,\sum C_j)$ and $P || (C_\max,C_j)$, respectively. For each problem, we explore its inapproximability and develop an approximation algorithm with analysis of its worst performance.
Kedong Yan, Cliques for multi-term linearization of 0-1 multilinear program for boolean logical pattern generation
Logical Analysis of Data (LAD) is a combinatorial optimization-based machine learning method. A key stage of LAD is pattern generation, where useful knowledge in a training dataset of two types of, say, + and − data under analysis is discovered. LAD pattern generation can be cast as a 0-1 multilinear program (MP) with a single 0-1 multilinear constraint: $$(PG): \max\limits_{x\in\{0,1\}^{2n}}f(x):=\sum_{i\in S^+}\Pi_{j\in J_i}(1-x_j)~~\text{subject to}~~g(x):=\sum_{i\in S^-}\Pi_{j\in J_i}(1-x_j)=0$$
The unconstrained maximization of $f$ (without $g$) is straightforward, thus the main difficulty of globally maximizing $(PG)$ arises primarily from the presence of g and the interaction between $f$ and $g$. We dealt with the task of linearizing $g$. Namely, we employed a graph theoretic analysis of data to discover sufficient conditions among neighboring data and also neighboring groups of data for ‘compactly linearizing’ $g$ in terms of a small number of stronger valid inequalities, as compared to those that can be obtained via 0-1 linearization techniques from the literature. In an earlier work, we analyzed + and − data (that is, terms of $f$ and $g$ together) on a graph to develop a polyhedral overestimation scheme for $f$. Extending this line of research, this paper proposes a new graph representation of monomials in f in conjunction with terms in $g$ to more aggressively aggregate a set of terms/data through each maximal clique in the graph into yielding a stronger valid inequality. This is achieved by means of a new notion of ‘neighbors’ that allows us to join two data that are more than 1-Hamming distance away from each other by an edge in the graph. We show that new inequalities generalize and subsume those from the earlier paper. Furthermore, with using six benchmark data mining datasets, we demonstrate that new inequalities are superior to their predecessors in terms of a more efficient global maximization of $(PG)$; that is, for a more efficient analysis and classification of real-life datasets.
Dabeen Lee (이다빈), Joint Chance-constrained programs and the intersection of mixing sets through a submodularity lens
The intersection of mixing sets with common binary variables arise when modeling joint linear chance-constrained programs with random right-hand sides and finite sample space. In this talk, we first establish a strong and previously unrecognized connection of mixing sets to submodularity. This viewpoint enables us to unify and extend existing results on polyhedral structures of mixing sets. Then we study the intersection of mixing sets with common binary variables and also linking constraint lower bounding a linear function of the continuous variables. We propose a new class of valid inequalities and characterize when this new class along with the mixing inequalities are sufficient to describe the convex hull.
Se-Young Yun (윤세영), Optimal sampling and clustering algorithms in the stochastic block model
This paper investigates the design of joint adaptive sampling and clustering algorithms in the Stochastic Block Model (SBM). To extract hidden clusters from the data, such algorithms sample edges sequentially in an adaptive manner, and after gathering edge samples, return cluster estimates. We derive information-theoretical upper bounds on the cluster recovery rate. These bounds reveal the optimal sequential edge sampling strategy, and interestingly, the latter does not depend on the sampling budget, but only the parameters of the SBM. We devise a joint sampling and clustering algorithm matching the recovery rate upper bounds. The algorithm initially uses a fraction of the sampling budget to estimate the SBM parameters, and to learn the optimal sampling strategy. This strategy then guides the remaining sampling process, which confers the optimality of the algorithm.
Hyung-Chan An (안형찬), Constant-factor approximation algorithms for parity-constrained facility location problems
Facility location is a prominent optimization problem that has inspired a large quantity of both theoretical and practical studies in combinatorial optimization. Although the problem has been investigated under various settings reflecting typical structures within the optimization problems of practical interest, little is known on how the problem behaves in conjunction with parity constraints. This shortfall of understanding was rather disturbing when we consider the central role of parity in the field of combinatorics. In this paper, we present the first constant-factor approximation algorithm for the facility location problem with parity constraints. We are given as the input a metric on a set of facilities and clients, the opening cost of each facility, and the parity requirement—$\mathsf{odd}$, $\mathsf{even}$, or $\mathsf{unconstrained}$—of every facility in this problem. The objective is to open a subset of facilities and assign every client to an open facility so as to minimize the sum of the total opening costs and the assignment distances, but subject to the condition that the number of clients assigned to each open facility must have the same parity as its requirement. Although the unconstrained facility location problem as a relaxation for this parity-constrained generalization has unbounded gap, we demonstrate that it yields a structured solution whose parity violation can be corrected at small cost. This correction is prescribed by a $T$-join on an auxiliary graph constructed by the algorithm. This graph does not satisfy the triangle inequality, but we show that a carefully chosen set of shortcutting operations leads to a cheap and sparse $T$-join. Finally, we bound the correction cost by exhibiting a combinatorial multi-step construction of an upper bound. At the end of this paper, we also present the first constant-factor approximation algorithm for the parity-constrained $k$-center problem, the bottleneck optimization variant.
Dong Yeap Kang (강동엽), On minimal highly connected spanning subgraphs in dense digraphs
In 1985, Mader showed that every $n(\geq4k+3)$-vertex strongly $k$-connected digraph contains a spanning strongly $k$-connected subgraph with at most $2kn-2k^2$ edges, and the only extremal digraph is a complete bipartite digraph $DK_{k,n−k}$. Nevertheless, since the extremal graph is sparse, Bang-Jensen asked whether there exists g(k) such that every strongly $k$-connected $n$-vertex tournament contains a spanning strongly $k$-connected subgraph with $kn + g(k)$ edges, which is an “almost $k$-regular” subgraph.
Recently, the question of Bang-Jensen was answered in the affirmative with $g(k) = O(k^2\log k)$, which is best possible up to logarithmic factor. In this talk, we discuss how to find minimal highly connected spanning subgraphs in dense digraphs as well as tournaments. In particular, we show that every highly connected dense digraph contains a spanning highly connected subgraph that is almost $k$-regular, which yields $g(k) = O(k^2)$ that is best possible for tournaments.
Tony Huynh, Stable sets in graphs with bounded odd cycle packing number
It is a classic result that the maximum weight stable set problem is efficiently solvable for bipartite graphs. The recent bimodular algorithm of Artmann, Weismantel and Zenklusen shows that it is also efficiently solvable for graphs without two disjoint odd cycles. The complexity of the stable set problem for graphs without $k$ disjoint odd cycles is a long-standing open problem for all other values of $k$. We prove that under the additional assumption that the input graph is embedded in a surface of bounded genus, there is a polynomial-time algorithm for each fixed $k$. Moreover, we obtain polynomial-size extended formulations for the respective stable set polytopes. To this end, we show that 2-sided odd cycles satisfy the Erdos-Posa property in graphs embedded in a fixed surface. This result may be of independent interest and extends a theorem of Kawarabayashi and Nakamoto asserting that odd cycles satisfy the Erdos-Posa property in graphs embedded in a fixed orientable surface Eventually, our findings allow us to reduce the original problem to the problem of finding a minimum-cost non-negative integer circulation of a certain homology class, which we prove to be efficiently solvable in our case.
Sang-il Oum, Rank-width: Algorithmic and structural results
Rank-width is a width parameter of graphs describing whether it is possible to decompose a graph into a tree-like structure by ‘simple’ cuts. This talk aims to survey known algorithmic and structural results on rank-width of graphs. This talk is based on a survey paper with further remarks on the recent developments.
On July 16, 2019, Dabeen Lee (이다빈) from IBS Discrete Mathematics Group presented his work on identifying a clutter minor related to ideal clutters at the Discrete Math Seminar. The title of his talk was “Integrality of set covering polyhedra and clutter minors”.
Given a finite set of elements $V$ and a family $\mathcal{C}$ of subsets of $V$, the set covering problem is to find a minimum cardinality subset of $V$ intersecting every subset in the family $\mathcal{C}$. The set covering problem, also known as the hitting set problem, admits a simple integer linear programming formulation. The constraint system of the integer linear programming formulation defines a polyhedron, and we call it the set covering polyhedron of $\mathcal{C}$. We say that a set covering polyhedron is integral if every extreme point is an integer lattice point. Although the set covering problem is NP-hard in general, conditions under which the problem becomes polynomially solvable have been studied. If the set covering polyhedron is integral, then it is straightforward that the problem can be solved using a polynomial-time algorithm for linear programming.
In this talk, we will focus on the question of when the set covering polyhedron is integral. We say that the family $\mathcal{C}$ is a clutter if every subset in $\mathcal{C}$ is inclusion-wise minimal. As taking out non-minimal subsets preserves integrality, we may assume that $\mathcal{C}$ is a clutter. We call $\mathcal{C}$ ideal if the set covering polyhedron of it is integral. To understand when a clutter is ideal, the notion of clutter minors is important in that $\mathcal{C}$ is ideal if and only if so is every minor of it. We will study two fundamental classes of non-ideal clutters, namely, deltas and the blockers of extended odd holes. We will characterize when a clutter contains either a delta or the blocker of an extended odd hole as a minor.
This talk is based on joint works with Ahmad Abdi and Gérard Cornuéjols.
