Notes on L-/M-convex functions and the separation theorems

The concepts of L-convex function and M-convex function have recently been introduced by Murota as generalizations of submodular function and base polyhedron, respectively, and discrete separation theorems are established for L-convex/concave functions and for M-convex/concave functions as generalizations of Frank’s discrete separation theorem for submodular/supermodular set functions and Edmonds’ matroid intersection theorem. This paper shows the equivalence between Murota’s L-convex functions and Favati and Tardella’s submodular integrally convex functions, and also gives alternative proofs of the separation theorems that provide a geometric insight by relating them to the ordinary separation theorem in convex analysis. Received: November 27, 1997 / Accepted: December 16, 1999?Published online May 12, 2000     

A capacity scaling algorithm for convex cost submodular flows

This paper presents a scaling scheme for submodular functions. A small but strictly submodular function is added before scaling so that the resulting functions should be submodular. This scaling scheme leads to a weakly polynomial algorithm to solve minimum cost integral submodular flow problems with separable convex cost functions, provided that an oracle for exchange capacities is available.     

The expressive power of binary submodular functions

We investigate whether all Boolean submodular functions can be decomposed into a sum of binary submodular functions over a possibly larger set of variables. This question has been considered in several different contexts in computer science, including computer vision, artificial intelligence, and pseudo-Boolean optimisation. Using a connection between the expressive power of valued constraints and certain algebraic properties of functions, we answer this question negatively.Our results have several corollaries. First, we characterise precisely which submodular polynomials of arity 4 can be expressed by binary submodular polynomials. Next, we identify a novel class of submodular functions of arbitrary arities which can be expressed by binary submodular functions, and therefore minimised efficiently using a so-called expressibility reduction to the Min-Cut problem. More importantly, our results imply limitations on this kind of reduction and establish, for the first time, that it cannot be used in general to minimise arbitrary submodular functions. Finally, we refute a conjecture of Promislow and Young on the structure of the extreme rays of the cone of Boolean submodular functions.     

Clique partitioning with value-monotone submodular cost

We consider the problem of partitioning a graph into cliques of bounded cardinality. The goal is to find a partition that minimizes the sum of clique costs where the cost of a clique is given by a set function on the nodes. We present a general algorithmic solution based on solving the problem variant without the cardinality constraint. We obtain constant factor approximations depending on the solvability of this relaxation for a large class of submodular cost functions which we call value-monotone submodular functions. For special graph classes we give optimal algorithms.     

Optimization over the polyhedron determined by a submodular function on a co-intersecting family

A greedy algorithm solves the problem of maximizing a linear objective function over the polyhedron (called the submodular polyhedron) determined by a submodular function on a distributive lattice or a ring family. We generalize the problem by considering a submodular function on a co-intersecting family and give an algorithm for solving it. Here, simple-minded greedy augmentations do not work any more and some complicated augmentations with multiple exchanges are required. We can find an optimal solution by at most 1/2n(n – 1) augmentations, wheren is the number of the variables and we assume a certain oracle for computing multiple exchange capacities.     

On the subdifferential of a submodular function

The author recently introduced a concept of a subdifferential of a submodular function defined on a distributive lattice. Each subdifferential is an unbounded polyhedron. In the present paper we determine the set of all the extreme points and rays of each subdifferential and show the relationship between subdifferentials of a submodular function and subdifferentials, in an ordinary sense of convex analysis, of Lovász's extension of the submodular function. Furthermore, for a modular function on a distributive lattice we give an algorithm for determining which subdifferential contains a given vector and finding a nonnegative linear combination of extreme vectors of the subdifferential which expresses the given vector minus the unique extreme point of the subdifferential.     

Generalized polymatroids and submodular flows

Polyhedra related to matroids and sub- or supermodular functions play a central role in combinatorial optimization. The purpose of this paper is to present a unified treatment of the subject. The structure of generalized polymatroids and submodular flow systems is discussed in detail along with their close interrelation. In addition to providing several applications, we summarize many known results within this general framework.Supported by a grant from the Alexander von Humboldt Stiftung and by the Institut für Ökonometrie und Operations Research of the University of Bonn, Bonn, Nassestr. 2, Federal Republic of Germany.     

Polybasic polyhedra: structure of polyhedra with edge vectors of support size at most 2

It has widely been recognized that submodular set functions and base polyhedra associated with them play fundamental and important roles in combinatorial optimization problems. In the present paper, we introduce a generalized concept of base polyhedron. We consider a class of pointed convex polyhedra in R^V whose edge vectors have supports of size at most 2. We call such a convex polyhedron a polybasic polyhedron. The class of polybasic polyhedra includes ordinary base polyhedra, submodular/supermodular polyhedra, generalized polymatroids, bisubmodular polyhedra, polybasic zonotopes, boundary polyhedra of flows in generalized networks, etc. We show that for a pointed polyhedron PR^V, the following three statements are equivalent:

(1) P is a polybasic polyhedron.

(2) Each face of P with a normal vector of the full support V is obtained from a base polyhedron by a reflection and scalings along axes.

(3) The support function of P is a submodular function on each orthant of R^V.

This reveals the geometric structure of polybasic polyhedra and its relation to submodularity.     

Minimization on submodular flows

Edmonds and Giles [1] discuss functions on the arcs of a digraph satisfying submodular set constraints. We call such functions submodular flows, show that the difference of group-valued submodular flows is a network circulation in a certain auxiliary digraph, and derive a criterion for the existence of group-valued submodular flows. We develop a method for maximizing the value of a group-valued submodular flow in a specified arc and a negative circuit method for minimizing certain functions on ring-valued submodular flows. In particular, algebraic linear functions over modules and certain semimodules as well as quotients of linear functions over totally ordered, commutative fields can be minimized.     

Supermodularity in Various Partition Problems

Supermodular and submodular functions have attracted a great deal of attention since the seminal paper of Lovász. Recently, supermodular functions were studied in the context of some optimal partition problems. We completely answer a question arisen there whether a certain partition function is supermodular.     

A capacity scaling algorithm for M-convex submodular flow

This paper presents a faster algorithm for the M-convex submodular flow problem, which is a generalization of the minimum-cost flow problem with an M-convex cost function for the flow-boundary, where an M-convex function is a nonlinear nonseparable discrete convex function on integer points. The algorithm extends the capacity scaling approach for the submodular flow problem by Fleischer, Iwata and McCormick (2002) with the aid of a novel technique of changing the potential by solving maximum submodular flow problems.Mathematics Subject Classification (1991): 90C27A preliminary version of this paper has appeared in Proceedings of the Tenth International Conference on Integer Programming and Combinatorial Optimization (IPCO X), LNCS 3064, Springer-Verlag, 2004, pp. 352–367.     

Algebraic and topological closure conditions for classes of pseudo-Boolean functions

We examine classes of real-valued functions of 0-1 variables closed under algebraic operations as well as topological convergence, and having a certain local characteristic (requiring that any function not in the class should have a k-variable minor not belonging to this class). It is shown that for k=2, the only 4 maximal classes with these properties are those of submodular, supermodular, monotone increasing and monotone decreasing functions. All the 13 locally defined closed classes are determined and shown to be intersections of the 4 maximal ones. All maximal classes for k≥3 are determined and characterized by the sign of higher order derivatives of the functions in the class.     

带基数约束的次模+超模(BP)函数最大化问题的流算法

本文研究在基数约束下具有单调性的次模+超模函数最大化问题的流模型。该问题在数据处理、机器学习和人工智能等方面都有广泛应用。借助于目标函数的收益递减率（$\gamma$）,我们设计了单轮读取数据的过滤-流算法,并结合次模、超模函数的全局曲率（$\kappa^{g}$）得到算法的近似比为$\min\left\{\frac{（1-\varepsilon）\gamma}{2^{\gamma}},1-\frac{\gamma}{2^{\gamma}（1-\kappa^{g}）^{2}}\right\}$。数值实验验证了过滤-流算法对BP最大化问题的有效性并且得出：次模函数和超模函数在同量级条件下,能保证在较少的时间内得到与贪婪算法相同的最优值。     

Decomposition of submodular functions

A decomposition theory for submodular functions is described. Any such function is shown to have a unique decomposition consisting of indecomposable functions and certain highly decomposable functions, and the latter are completely characterized. Applications include decompositions of hypergraphs based on edge and vertex connectivity, the decomposition of matroids based on three-connectivity, the Gomory—Hu decomposition of flow networks, and Fujishige’s decomposition of symmetric submodular functions. Efficient decomposition algorithms are also discussed.     

Directed submodularity,ditroids and directed submodular flows

Set relations and operations such as inclusion, union and intersection are generalized to directed subsets whose elements are distinguished between forward and backward elements. The concepts of submodular functions, matroids and polymatroidal network flows are extended to the concepts of directed submodular functions, ditroids and directed submodular flows on directed subsets. Two unrelated matroids (submodular functions) can be embedded in one ditroid (directed submodular function). Total dual integrality is preserved in these generalizations and proved for very general set-function class-directed odd submodular functions.This work was partially supported by Chinese National Natural Science Fund.     

带基数约束的次模+超模(BP)函数最大化问题的流算法

本文研究在基数约束下具有单调性的次模+超模函数最大化问题的流模型。该问题在数据处理、机器学习和人工智能等方面都有广泛应用。借助于目标函数的收益递减率（$\gamma$）,我们设计了单轮读取数据的过滤-流算法,并结合次模、超模函数的全局曲率（$\kappa^{g}$）得到算法的近似比为$\min\left\{\frac{（1-\varepsilon）\gamma}{2^{\gamma}},1-\frac{\gamma}{2^{\gamma}（1-\kappa^{g}）^{2}}\right\}$。数值实验验证了过滤-流算法对BP最大化问题的有效性并且得出：次模函数和超模函数在同量级条件下,能保证在较少的时间内得到与贪婪算法相同的最优值。     

Maximizing non-monotone submodular set functions subject to different constraints: Combined algorithms

We study the problem of maximizing constrained non-monotone submodular functions and provide approximation algorithms that improve existing algorithms in terms of either the approximation factor or simplicity. Different constraints that we study are exact cardinality and multiple knapsack constraints for which we achieve (0.25−?)-factor algorithms.We also show, as our main contribution, how to use the continuous greedy process for non-monotone functions and, as a result, obtain a 0.13-factor approximation algorithm for maximization over any solvable down-monotone polytope.     

Minimizing symmetric submodular functions

We describe a purely combinatorial algorithm which, given a submodular set functionf on a finite setV, finds a nontrivial subsetA ofV minimizingf[A] + f[V A]. This algorithm, an extension of the Nagamochi—Ibaraki minimum cut algorithm as simplified by Stoer and Wagner [M. Stoer, F. Wagner, A simple min cut algorithm, Proceedings of the European Symposium on Algorithms ESA '94, LNCS 855, Springer, Berlin, 1994, pp. 141–147] and by Frank [A. Frank, On the edge-connectivity algorithm of Nagamochi and Ibaraki, Laboratoire Artémis, IMAG, Université J. Fourier, Grenbole, 1994], minimizes any symmetric submodular function using O(|V|³) calls to a function value oracle. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.A preliminary version of this paper was presented at the Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) in January 1995. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.     

Greedy approximations for minimum submodular cover with submodular cost

It is well-known that a greedy approximation with an integer-valued polymatroid potential function f is H(γ)-approximation of the minimum submodular cover problem with linear cost where γ is the maximum value of f over all singletons and H(γ) is the γ-th harmonic number. In this paper, we establish similar results for the minimum submodular cover problem with a submodular cost (possibly nonlinear) and/or fractional submodular potential function f.     

Maximization of submodular functions: Theory and enumeration algorithms

Submodular functions are powerful tools to model and solve either to optimality or approximately many operational research problems including problems defined on graphs. After reviewing some long-standing theoretical results about the structure of local and global maxima of submodular functions, Cherenin’s selection rules and his Dichotomy Algorithm, we revise the above mentioned theory and show that our revision is useful for creating new non-binary branching algorithms and finding either approximation solutions with guaranteed accuracy or exact ones.