Polyhedral Combinatorics Research Articles

Extended formulations for binary optimal control problems

AbstractExtended formulations are an important tool in polyhedral combinatorics. Many combinatorial optimization problems require an exponential number of inequalities when modeled as a linear program in the natural space of variables. However, by adding artificial variables, one can often find a small linear formulation, i.e., one containing a polynomial number of variables and constraints, such that the projection to the original space of variables yields a perfect linear formulation. Motivated by binary optimal control problems with switching constraints, we show that a similar approach can be useful also for optimization problems in function space, in order to model the closed convex hull of feasible controls in a compact way. More specifically, we present small extended formulations for switches with bounded variation and for dwell-time constraints. For general linear switching point constraints, we devise an extended model linearizing the problem, but show that a small extended formulation that is compatible with discretization cannot exist unless P = NP.

Lifts for Voronoi Cells of Lattices

Many polytopes arising in polyhedral combinatorics are linear projections of higher-dimensional polytopes with significantly fewer facets. Such lifts may yield compressed representations of polytopes, which are typically used to construct small-size linear programs. Motivated by algorithmic implications for the closest vector problem, we study lifts of Voronoi cells of lattices. We construct an explicit d-dimensional lattice such that every lift of the respective Voronoi cell has 2^{Omega (d/{log d})} facets. On the positive side, we show that Voronoi cells of d-dimensional root lattices and their dual lattices have lifts with {{mathcal {O}}}(d) and {{mathcal {O}}}(d log d) facets, respectively. We obtain similar results for spectrahedral lifts.

Sublinear Circuits for Polyhedral Sets

Sublinear circuits are generalizations of the affine circuits in matroid theory, and they arise as the convex-combinatorial core underlying constrained non-negativity certificates of exponential sums and of polynomials based on the arithmetic-geometric inequality. Here, we study the polyhedral combinatorics of sublinear circuits for polyhedral constraint sets. We give results on the relation between the sublinear circuits and their supports and provide necessary as well as sufficient criteria for sublinear circuits. Based on these characterizations, we provide some explicit results and enumerations for two prominent polyhedral cases, namely the non-negative orthant and the cube [− 1,1]n.

Relationship of two formulations for shortest bibranchings

The shortest bibranching problem is a common generalization of the minimum-weight edge cover problem in bipartite graphs and the minimum-weight arborescence problem in directed graphs. For the shortest bibranching problem, an efficient primal-dual algorithm is given by Keijsper and Pendavingh (J Comb Theory Ser B 73:130–145, 1998), and the tractability of the problem is ascribed to total dual integrality in a linear programming formulation by Schrijver (Ann Discret Math 16:261–280, 1982). Another view on the tractability of this problem is afforded by a valuated matroid intersection formulation by Takazawa (Jpn J Ind Appl Math 29:561–573, 2012). In the present paper, we discuss the relationship between these two formulations for the shortest bibranching problem. We first demonstrate that the valuated matroid intersection formulation can be derived from the linear programming formulation through the Benders decomposition, where integrality is preserved in the decomposition process and the resulting convex programming is endowed with discrete convexity. We then show how a pair of primal and dual optimal solutions of one formulation is constructed from that of the other formulation, thereby providing a connection between polyhedral combinatorics and discrete convex analysis.

On the Balanced Minimum Evolution polytope

Recent advances on the polyhedral combinatorics of the Balanced Minimum Evolution Problem (BMEP) enabled the characterization of a number of facets of its convex hull (also referred to as the BMEP polytope) as well as the discovery of connections between this polytope and the permutoassociahedron. In this article, we extend these studies, by presenting new results concerning some fundamental characteristics of the BMEP polytope, new facet-defining inequalities in the case of six or more taxa, a number of valid inequalities, and a polynomial time oracle to recognize its vertices. Our aim is to broaden understanding of the polyhedral combinatorics of the BMEP with a view to developing new and more effective exact solution algorithms.

A Notion of Total Dual Integrality for Convex, Semidefinite, and Extended Formulations

Total dual integrality is a powerful and unifying concept in polyhedral combinatorics and integer programming that enables the refinement of geometric min-max relations given by linear programming ...

Opportunistic Topological Interference Management

The topological interference management (TIM) problem studies the degrees of freedom (DoF) of partially-connected interference networks with no channel state information (CSI) at the transmitters except the network topology (i.e., partial connectivity). In this paper, we consider a variant of the TIM problem with uncertainty in network topology, where the channel state with partial connectivity is only known to belong to one of $M$ states at the transmitters. In particular, the transmitter has access to all network topological information over $M$ states, but is unaware of which state it falls in exactly for communication. The receiver at any state is aware of the exact state it falls in besides the network topologies of all states, and wish to recover as much highly-prioritized information at current state as possible. We formulate it as the opportunistic TIM problem with network uncertainty modeled by $M$ state-varying network topologies. To adapt to network topology uncertainty and different message decoding priority, joint encoding and opportunistic decoding are enabled at the transmitters and receivers respectively. Specifically, being aware of all possible network topologies, each transmitter sends a signal jointly encoded from all messages desired over $M$ states, say $M$ distinct messages, and at a certain State $m$ , Receiver $k$ wishes to opportunistically decode the first $\pi _{k}(m) \in \{1,2,\cdots,M\}$ higher-priority messages. Under this opportunistic TIM setting, we construct a multi-state conflict graph to capture the mutual conflict of messages over $M$ states, and characterize the optimal DoF region of two classes of network topologies via polyhedral combinatorics. A remarkable fact is that, under an additional mild monotonous condition, the optimality conditions of orthogonal access and one-to-one interference alignment still apply to TIM with uncertainty in network topology.

The Computational Complexity of Integer Programming with Alternations

We prove that integer programming with three alternating quantifiers is NP-complete, even for a fixed number of variables. This complements earlier results by Lenstra [ 16 ] [Lenstra H ( 1983 ) Integer programming with a fixed number of variables. Math. Oper. Res. 8(4):538–548.] and Kannan [ 13, 14 ] [Kannan R ( 1990 ) Test sets for integer programs, ∀ ∃ sentences. Polyhedral Combinatorics (American Mathematical Society, Providence, RI), 39–47. Kannan R ( 1992 ) Lattice translates of a polytope and the Frobenius problem. Combinatorica 12(2):161–177.], which together say that integer programming with at most two alternating quantifiers can be done in polynomial time for a fixed number of variables. As a byproduct of the proof, we show that for two polytopes [Formula: see text], counting the projections of integer points in Q\P is #P-complete. This contrasts the 2003 result by Barvinok and Woods [ 5 ] [Barvinok A, Woods K ( 2003 ) Short rational generating functions for lattice point problems. J. Amer. Math. Soc. 16(4):957–979.], which allows counting in polynomial time the projections of integer points in P and Q separately.

Enumerating vertices of the balanced minimum evolution polytope

Recent advances on the polyhedral combinatorics of the Balanced Minimum Evolution Problem (BMEP) enabled the identification of fundamental characteristics of the convex hull of the BMEP (or BMEP polytope) as well as the description of some of its facets. These results have been possible thanks to the assistance of general purpose softwares for polyhedral analyses, such as Polymake, whose running times however become quickly unpractical. In this article, we address the problem of designing tailored algorithms for the study of the BMEP polytope, with special focus on the problem of enumerating its vertices. To this end, by exploiting some results from the polyhedral combinatorics of the BMEP and a particular encoding scheme based on Prüfer coding, we present an iterative enumeration algorithm that is markedly faster than current general purpose softwares and that can be natively massively parallelized.

Extended formulations for order polytopes through network flows

Mathematical psychology has a long tradition of modeling probabilistic choice via distribution-free random utility models and associated random preference models. For such models, the predicted choice probabilities often form a bounded and convex polyhedral set, or polytope. Polyhedral combinatorics have thus played a key role in studying the mathematical structure of these models. However, standard methods for characterizing the polytopes of such models are subject to a combinatorial explosion in complexity as the number of choice alternatives increases. Specifically, this is the case for random preference models based on linear, weak, semi- and interval orders. For these, a complete, linear description of the polytope is currently known only for, at most, 5–8 choice alternatives. We leverage the method of extended formulations to break through those boundaries. For each of the four types of preferences, we build an appropriate network, and show that the associated network flow polytope provides an extended formulation of the polytope of the choice model. This extended formulation has a simple linear description that is more parsimonious than descriptions obtained by standard methods for large numbers of choice alternatives. The result is a computationally less demanding way of testing the probabilistic choice model on data. We sketch how the latter interfaces with recent developments in contemporary statistics.

Topological Interference Management With Decoded Message Passing

The topological interference management (TIM) problem studies partially-connected interference networks with no channel state information except for the network topology (i.e., connectivity graph) at the transmitters. In this paper, we consider a similar problem in the uplink cellular networks, while message passing is enabled at the receivers (e.g., base stations), so that the decoded messages can be routed to other receivers via backhaul links to help further improve network performance. For this TIM problem with decoded message passing (TIM-MP), we model the interference pattern by conflict digraphs, connect orthogonal access to the acyclic set coloring on conflict digraphs, and show that one-to-one interference alignment boils down to orthogonal access because of message passing. With the aid of polyhedral combinatorics, we identify the structural properties of certain classes of network topologies where orthogonal access achieves the optimal degrees-of-freedom (DoF) region in the information-theoretic sense. The relation to the conventional index coding with simultaneous decoding is also investigated by formulating a generalized index coding problem with successive decoding as a result of decoded message passing. The properties of reducibility and criticality are also studied, by which we are able to prove the linear optimality of orthogonal access in terms of symmetric DoF for the networks up to four users with all possible network topologies (218 instances). Practical issues of the tradeoff between the overhead of message passing and the achievable symmetric DoF are also discussed, in the hope of facilitating efficient backhaul utilization.

On 2-Level Polytopes Arising in Combinatorial Settings

2-level polytopes naturally appear in several areas of pure and applied mathematics, including combinatorial optimization, polyhedral combinatorics, communication complexity, and statistics. In this paper, we present a study of some 2-level polytopes arising in combinatorial settings. Our first contribution is proving that v(P)*f(P) is upper bounded by d*2^(d+1), for a large collection of families of such polytopes P. Here v(P) (resp. f(P)) is the number of vertices (resp. facets) of P, and d is its dimension. Whether this holds for all 2-level polytopes was asked in [Bohn et al., ESA 2015], and experimental results from [Fiorini et al., ISCO 2016] showed it true up to dimension 7. The key to most of our proofs is a deeper understanding of the relations among those polytopes and their underlying combinatorial structures. This leads to a number of results that we believe to be of independent interest: a trade-off formula for the number of cliques and stable sets in a graph; a description of stable matching polytopes as affine projections of certain order polytopes; and a linear-size description of the base polytope of matroids that are 2-level in terms of cuts of an associated tree.

On facet-inducing inequalities for combinatorial polytopes

One of the central questions of polyhedral combinatorics is the question of the algorithmic relationship between the vertex and facet descriptions of convex polytopes. From the standpoint of combinatorial optimization, the main reason for the actuality of this question is the possibility of applying the methods of convex analysis to solving the extremal combinatorial problems. In this paper, we consider the combinatorial polytopes of a sufficiently general form. We obtain a few of necessary conditions and a sufficient condition for a supporting inequality of a polytope to be a facet inequality and give an illustration of the use of the developed technology to the polytope of some graph approximation problem.

When Is the Matching Polytope Box-Totally Dual Integral?

Let G = (V, E) be a graph. The matching polytope of G, denoted by P(G), is the convex hull of the incidence vectors of all matchings in G. As proved by Edmonds [10] [Edmonds J (1965) Maximum matching and a polyhedron with 0, 1-vertices, J. Res. Nat. Bur. Standards Sect. B 69(1–2):125–130.], P(G) is determined by the following linear system π(G): x(e) ≥ 0 for each e ∈ E; x(δ(v)) ≤ 1 for each v ∈ V; and x(E[U]) ≤ ½|U|⌋ for each U ⊆ V with |U| odd. In 1978, Cunningham and Marsh [6] [Cunningham W, Marsh A (1978) A primal algorithm for optimum matching. Balinski ML, Hoffman AJ, eds. Polyhedral combinatorics. Mathematical Programming Studies, Vol. 8 (Springer, Berlin), 50–72.] strengthened this theorem by showing that π(G) is always totally dual integral. In 1984, Edmonds and Giles [11] [Edmonds J, Giles R (1984) Total dual integrality of linear inequality systems. Progress in Combinatorial Optimization (Academic Press, Toronto), 117–129.] initiated the study of graphs G for which π(G) is box-totally dual integral. In this paper, we present a structural characterization of all such graphs, and develop a general and powerful method for establishing box-total dual integrality. The online appendix is available at https://doi.org/10.1287/moor.2017.0852 .

Efficient implementation of Carathéodory’s theorem for the single machine scheduling polytope

In a fundamental paper in polyhedral combinatorics, Queyranne describes the complete facial structure of a classical object in combinatorial optimization, the single machine scheduling polytope. In the same paper, he answers essentially all relevant algorithmic questions with respect to optimization and separation. In the present paper, motivated by recent applications in the design of optimal incentive compatible mechanisms, we address an algorithmic question that was apparently not addressed before. Namely, we turn Carathéodory’s theorem into an algorithm, and ask to write an arbitrary point in the scheduling polytope as a convex combination of the vertices of the polytope. We give a combinatorial O(n2) time algorithm, which is linear in the naive encoding of the output size. We obtain this result by exploiting the fact that the scheduling polytope is a zonotope, and by the observation that its barycentric subdivision has a simple, linear description. The actual decomposition algorithm is an implementation of a method proposed by Grötschel, Lovász and Schrijver, applied to one of the subpolytopes of the barycentric subdivision. We thereby also shed new light on an algorithm recently proposed for a special case, namely the permutahedron.

Polyhedral combinatorics of the [formula omitted]-partitioning problem with representative variables

The K-partitioning problem consists in partitioning the vertices of a weighted graph in K sets in order to minimize a function related to the edge weights. We introduce a linear mixed integer formulation with edge variables and representative variables. We investigate the polyhedral combinatorics of the problem, study several families of facet-defining inequalities and evaluate their efficiency on the linear relaxation.

Computing a Nonnegative Matrix Factorization---Provably

In the nonnegative matrix factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$, where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m$, respectively. In some applications, it makes sense to ask instead for the product $AW$ to approximate $M$, i.e. (approximately) minimize $\left\lVert{M - AW}_F\right\rVert$, where $\left\lVert\right\rVert_F$, denotes the Frobenius norm; we refer to this as approximate NMF. This problem has a rich history spanning quantum mechanics, probability theory, data analysis, polyhedral combinatorics, communication complexity, demography, chemometrics, etc. In the past decade NMF has become enormously popular in machine learning, where $A$ and $W$ are computed using a variety of local search heuristics. Vavasis recently proved that this problem is NP-complete. (Without the restriction that $A$ and $W$ be nonnegative, both the exact and approximate problems can be solved optimally via the singular value decomposition.) We initiate a study of when this problem is solvable in polynomial time. Our results are the following: 1. We give a polynomial-time algorithm for exact and approximate NMF for every constant $r$. Indeed NMF is most interesting in applications precisely when $r$ is small. 2. We complement this with a hardness result, that if exact $NMF$ can be solved in time $(nm)^{o(r)}$, 3-SAT has a subexponential-time algorithm. This rules out substantial improvements to the above algorithm. 3. We give an algorithm that runs in time polynomial in $n$, $m$, and $r$ under the separablity condition identified by Donoho and Stodden in 2003. The algorithm may be practical since it is simple and noise tolerant (under benign assumptions). Separability is believed to hold in many practical settings. To the best of our knowledge, this last result is the first example of a polynomial-time algorithm that provably works under a non-trivial condition on the input and we believe that this will be an interesting and important direction for future work.

An extension of Lehman’s theorem and ideal set functions

Lehman’s theorem on the structure of minimally nonideal clutters is a fundamental result in polyhedral combinatorics. One approach to extending it has been to give a common generalization with the characterization of minimally imperfect clutters (Sebö, 1998; Gasparyan et al., 2003). We give a new generalization of this kind, which combines two types of covering inequalities and works well with the natural definition of minors. We also show how to extend the notion of idealness to unit-increasing set functions, in a way that is compatible with minors and blocking operations.

A branch-price-and-cut algorithm for the minimum evolution problem

We investigate the Minimum Evolution Problem (MEP), an NP-hard network design problem arising from computational biology. The MEP consists in finding a weighted unrooted binary tree having n leaves, minimal length, and such that the sum of the edge weights belonging to the unique path between each pair of leaves is greater than or equal to a prescribed value. We study the polyhedral combinatorics of the MEP and investigate its relationships with the Balanced Minimum Evolution Problem. We develop an exact solution approach for the MEP based on a nontrivial combination of a parallel branch-price-and-cut scheme and a non-isomorphic enumeration of all possible solutions to the problem. Computational experiments show that the new solution approach outperforms the best mixed integer linear programming formulation for the MEP currently described in the literature. Our results give a perspective on the combinatorics of the MEP and suggest new directions for the development of future exact solution approaches that may turn out useful in practical applications. We also show that the MEP is statistically consistent.

Extended formulations, nonnegative factorizations, and randomized communication protocols

An extended formulation of a polyhedron $$P$$P is a linear description of a polyhedron $$Q$$Q together with a linear map $$\pi $$? such that $$\pi (Q)=P$$?(Q)=P. These objects are of fundamental importance in polyhedral combinatorics and optimization theory, and the subject of a number of studies. Yannakakis' factorization theorem (Yannakakis in J Comput Syst Sci 43(3):441---466, 1991) provides a surprising connection between extended formulations and communication complexity, showing that the smallest size of an extended formulation of $$P$$P equals the nonnegative rank of its slack matrix $$S$$S. Moreover, Yannakakis also shows that the nonnegative rank of $$S$$S is at most $$2^c$$2c, where $$c$$c is the complexity of any deterministic protocol computing $$S$$S. In this paper, we show that the latter result can be strengthened when we allow protocols to be randomized. In particular, we prove that the base-$$2$$2 logarithm of the nonnegative rank of any nonnegative matrix equals the minimum complexity of a randomized communication protocol computing the matrix in expectation. Using Yannakakis' factorization theorem, this implies that the base-$$2$$2 logarithm of the smallest size of an extended formulation of a polytope $$P$$P equals the minimum complexity of a randomized communication protocol computing the slack matrix of $$P$$P in expectation. We show that allowing randomization in the protocol can be crucial for obtaining small extended formulations. Specifically, we prove that for the spanning tree and perfect matching polytopes, small variance in the protocol forces large size in the extended formulation.