Approximating max-cut under graph-MSO constraints

We consider the max-cut and max-$k$-cut problems under graph-based constraints. Our approach can handle any constraint specified using monadic second-order (MSO) logic on graphs of constant treewidth. We give a $\frac{1}{2}$-approximation algorithm for this class of problems.     

One-third-integrality in the max-cut problem

Given a graphG = (V, E), the metric polytopeS (G) is defined by the inequalitiesx(F) – x(CF) |F| – 1 for , |F| odd,C cycle ofG, and 0 x _e 1 fore E. Optimization overS (G) provides an approximation for the max-cut problem. The graphG is called 1/d-integral if all the vertices ofS(G) have their coordinates in{i/d 0 i d}. We prove that the class of 1/d-integral graphs is closed under minors, and we present several minimal forbidden minors for 1/3-integrality. In particular, we characterize the 1/3-integral graphs on seven nodes. We study several operations preserving 1/d-integrality, in particular, thek-sum operation for 0 k 3. We prove that series parallel graphs are characterized by the following stronger property. All vertices of the polytopeS (G) {x x u} are 1/3-integral for every choice of 1/3-integral bounds, u on the edges ofG. Research by this author was partially done at CWI in Amsterdam.Research by this author was done at the Institut für Diskrete Mathematik of Bonn, supported by the A. von Humboldt Foundation.Deceased on April 2nd, 1995.     

Stronger linear programming relaxations of max-cut

We consider linear programming relaxations for the max cut problem in graphs, based on k-gonal inequalities. We show that the integrality ratio for random dense graphs is asymptotically 1+1/k and for random sparse graphs is at least 1+3/k. There are O(n^k)k-gonal inequalities. These results generalize work by Poljak and Tuza, who gave similar results for k=3.     

Approximating Perpetuities

We propose and analyze an algorithm to approximate distribution functions and densities of perpetuities. Our algorithm refines an earlier approach based on iterating discretized versions of the fixed point equation that defines the perpetuity. We significantly reduce the complexity of the earlier algorithm. Also one particular perpetuity arising in the analysis of the selection algorithm Quickselect is studied in more detail. Our approach works well for distribution functions. For densities we have weaker error bounds although computer experiments indicate that densities can also be approximated well. Supported by an Emmy Noether Fellowship of the Deutsche Forschungsgemeinschaft.     

Partitioning planar graphs: a?fast combinatorial approach for max-cut

The max-cut problem asks for partitioning the nodes V of a graph G=(V,E) into two sets (one of which might be empty), such that the sum of weights of edges joining nodes in different partitions is maximum. Whereas for general instances the max-cut problem is NP-hard, it is polynomially solvable for certain classes of graphs. For planar graphs, there exist several polynomial-time methods determining maximum cuts for arbitrary choice of edge weights. Typically, the problem is solved by computing a minimum-weight perfect matching in some associated graph. The most efficient known algorithms are those of Shih et al. (IEEE Trans. Comput. 39(5):694–697, 1990) and that of Berman et al. (WADS, Lecture Notes in Computer Science, vol. 1663, pp. 25–36, Springer, Berlin, 1999). The running time of the former can be bounded by O(|V|^\frac32log|V|)O(|V|^{\frac{3}{2}}\log|V|). The latter algorithm is more generally for determining T-joins in graphs. Although it has a slightly larger bound on the running time of O(|V|^\frac32(log|V|)^\frac32)a(|V|)O(|V|^{\frac{3}{2}}(\log|V|)^{\frac{3}{2}})\alpha(|V|), where α(|V|) is the inverse Ackermann function, it can solve large instances in practice.     

Generalised 2-circulant inequalities for the max-cut problem

The max-cut problem is a fundamental combinatorial optimisation problem, with many applications. Poljak and Turzik found some facet-defining inequalities for the associated polytope, which we call 2-circulant inequalities. We present a more general family of facet-defining inequalities, an exact separation algorithm that runs in polynomial time, and some computational results.     

Approximating Planar Rotations

We study the problem of approximating a rotation of the plane, α :R ² R ² , α (x,y)=(x cos θ + y sin θ , y cos θ - x sin θ ), by a bijection β :Z ² Z ² . We show by an explicit construction that β may be chosen so that , where r= tan ( θ /2). The scheme is based on those invented and patented by the second author in 1994. Received November 21, 1996, and in revised form February 20, 1997.     

Approximating exponential models

Approximation of parametric statistical models by exponential models is discussed, from the viewpoints of observed as well as of expected likelihood geometry. This extends a construction, in expected geometry, due to Amari. The approximations considered are parametrization invariant and local. Some of them relate to conditional models given exact or approximate ancillary statistics. Various examples are considered and the relation between the maximum likelihood estimators of the original model and the approximating models is studied.Research partly supported by the Danish Science Research Council.     

Approximating Directed Multicuts

The seminal paper of Leighton and Rao (1988) and subsequent papers presented approximate min-max theorems relating multicommodity flow values and cut capacities in undirected networks, developed the divide-and-conquer method for designing approximation algorithms, and generated novel tools for utilizing linear programming relaxations. Yet, despite persistent research efforts, these achievements could not be extended to directed networks, excluding a few cases that are symmetric and therefore similar to undirected networks. This paper is an attempt to remedy the situation. We consider the problem of finding a minimum multicut in a directed multicommodity flow network, and give the first nontrivial upper bounds on the max flow-to-min multicut ratio. Our results are algorithmic, demonstrating nontrivial approximation guarantees.* Supported in part by NSERC research grant OGP0138432. Part of this work was done while visiting AT&T Labs–Research. Work at the Technion supported by Israel Science Foundation grant number 386/99, by BSF grants 96-00402 and 99-00217, by Ministry of Science contract number 9480198, by EU contract number 14084 (APPOL), by the CONSIST consortium (through the MAGNET program of the Ministry of Trade and Industry), and by the Fund for the Promotion of Research at the Technion.     

A discrete dynamic convexized method for the max-cut problem

The max-cut problem is a classical NP-hard problem in graph theory. In this paper, we adopt a local search method, called MCFM, which is a simple modification of the Fiduccia-Mattheyses heuristic method in Fiduccia and Mattheyses (Proc. ACM/IEEE DAC, pp. 175?C181, 1982) for the circuit partitioning problem in very large scale integration of circuits and systems. The method uses much less computational cost than general local search methods. Then, an auxiliary function is presented which has the same global maximizers as the max-cut problem. We show that maximization of the function using MCFM can escape successfully from previously converged discrete local maximizers by taking increasing values of a parameter. An algorithm is proposed for the max-cut problem, by maximizing the auxiliary function using MCFM from random initial solutions. Computational experiments were conducted on three sets of standard test instances from the literature. Experimental results show that the proposed algorithm is effective for the three sets of standard test instances.     

A discrete filled function algorithm for approximate global solutions of max-cut problems

A discrete filled function algorithm is proposed for approximate global solutions of max-cut problems. A new discrete filled function is defined for max-cut problems and the properties of the filled function are studied. Unlike general filled function methods, using the characteristic of max-cut problems, the parameters in proposed filled function need not be adjusted. This greatly increases the efficiency of the filled function method. By combining a procedure that randomly generates initial points for minimization of the filled function, the proposed algorithm can greatly reduce the calculation cost and be applied to large scale max-cut problems. Numerical results on different sizes and densities test problems indicate that the proposed algorithm is efficient and stable to get approximate global solutions of max-cut problems.     

Approximating absolute Galois groups

In this paper we identify a class of profinite groups (totally torsion free groups) that includes all separable Galois groups of fields containing an algebraically closed subfield, and demonstrate that it can be realized as an inverse limit of torsion free virtually finitely generated abelian (tfvfga) profinite groups. We show by examples that the condition is quite restrictive. In particular, semidirect products of torsion free abelian groups are rarely totally torsion free. The result is of importance for K-theoretic applications, since descent problems for tfvfga groups are relatively manageable.     

Approximating Rough Stochastic PDEs

We study approximations to a class of vector‐valued equations of Burgers type driven by a multiplicative space‐time white noise. A solution theory for this class of equations has been developed recently in Probability Theory Related Fields by Hairer and Weber. The key idea was to use the theory of controlled rough paths to give definitions of weak/mild solutions and to set up a Picard iteration argument. In this article the limiting behavior of a rather large class of (spatial) approximations to these equations is studied. These approximations are shown to converge and convergence rates are given, but the limit may depend on the particular choice of approximation. This effect is a spatial analogue to the Itô‐Stratonovich correction in the theory of stochastic ordinary differential equations, where it is well known that different approximation schemes may converge to different solutions.© 2014 Wiley Periodicals, Inc.     

A remark on max-cut problem with an application to digital-analogue convertors

We introduce notions of a distance-decreasing weight function and of an alternating cut. For a class of distance-decreasing weights we solve the max-cut problem. In general, we prove that alternating cuts are near optimal. This has application to digital-analogue convertors.