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Multi-Budgeted Matchings and Matroid Intersection Via Dependent Rounding

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Multi-Budgeted Matchings and Matroid Intersection Via Dependent Rounding Multi-budgeted Matchings and Matroid Intersection via Dependent Rounding Chandra Chekuri∗ Jan Vondrak´ y Rico Zenklusenz Abstract ing: given a fractional point x in a polytope P ⊂ Rn, ran- Motivated by multi-budgeted optimization and other applications, domly round x to a solution R corresponding to a vertex we consider the problem of randomly rounding a fractional solution of P . Here P captures the deterministic constraints that we x in the (non-bipartite graph) matching and matroid intersection wish the rounding to satisfy, and it is natural to assume that polytopes. We show that for any fixed δ > 0, a given point P is an integer polytope (typically a f0; 1g polytope). Of course the important issue is what properties we need R to x can be rounded to a random solution R such that E[1R] = (1 − δ)x and any linear function of x satisfies dimension-free satisfy, and this is dictated by the application at hand. A Chernoff-Hoeffding concentration bounds (the bounds depend on property that is useful in several applications is that R sat- δ and the expectation µ). We build on and adapt the swap isfies concentration properties for linear functions of x: that n rounding scheme in our recent work [9] to achieve this result. is, for any vector a 2 [0; 1] , we want the linear function P 1 Our main contribution is a non-trivial martingale based analysis a(R) = i2R ai to be concentrated around its expectation . framework to prove the desired concentration bounds. In this Ideally, we would like to have E[1R] = x, which would P paper we describe two applications. We give a randomized PTAS mean E[a(R)] = i aixi. If this is not feasible, we would for matroid intersection and matchings with any fixed number of like E[1R] to be approximately equal to x. budget constraints. We also give a deterministic PTAS for the case A natural question is the following. For which polytopes of matchings. The concentration bounds also yield related results P can we implement such a rounding procedure? A strong when the number of budget constraints is not fixed. As a second property that implies concentration for linear functions is 2 application we obtain an algorithm to compute in polynomial that of negative correlation , as shown by Panconesi and time an "-approximate Pareto-optimal set for the multi-objective Srinivasan [25]. In our recent work [9] we described a variants of these problems, when the number of objectives is a rounding scheme called randomized swap rounding, to unify fixed constant. We rely on a result of Papadimitriou and Yannakakis and generalize some existing results. In particular, we [26]. showed that if P is a matroid polytope (the convex hull of independent sets of the matroid), then one can round 1 Introduction x 2 P to a random independent set R such that E[1R] = x and the coordinates of 1 satisfy negative correlation. Randomized rounding of a fractional solution into an integral R We also showed negative correlation properties for certain solution is a powerful and ubiquitious technique in approxi- restricted subsets of variables when P is the intersection of mation algorithm design. Following the influential work of two matroids; this restricted setting nevertheless captures the Raghavan and Thompson [27] for routing, packing and cov- important and useful special case of the assignment polytope ering problems, several different randomized rounding meth- where one obtains negative correlation for the edge variables ods have been developed over the years. Dependent ran- incident to each vertex of the underlying bipartite graph domized rounding methods have recently found many ap- [14]. There are, however, applications where one requires plications [1, 31, 14, 11, 19, 3, 2, 29, 6]. The term depen- concentration properties for arbitrary linear functions of the dent refers to the property of the rounding scheme that en- variables. If one wants to use the property of negative sures that the rounded solution satisfies some additional con- correlation, then matroids are essentially the only structures straints in a deterministic fashion — this implies that the co- where this can be done: We have recently shown that a f0; 1g ordinates of the fractional solution cannot be independently polytope P has the property that any x 2 P can be rounded rounded. An abstract view of such a scheme is the follow- to a vertex R of P such that E[1R] = x and the coordinates of 1R are negatively correlated, if and only if P is an axis- ∗ Department of Computer Science, University of Illinois, Urbana, IL parallel projection of a matroid base polytope [10]. 61801. Supported in part by NSF grants CCF-0728782 and CCF-1016684. E-mail: [email protected]. yIBM Almaden Research Center, San Jose, CA 95120. E-mail: 1In this paper we focus on non-negative vectors a 2 [0; 1]n although [email protected]. there are applications for vectors a 2 [−1; 1]n (see [1]). z 2 Dept. of Mathematics, MIT, Cambridge, MA 02139. E-mail: We call f0; 1g-random variables X1;:::;Xr negatively correlated, Q Q Q Q [email protected]. Supported by the Swiss National Science Foun- if E[ i2T Xi] ≤ i2T E[Xi] and E[ i2T (1 − Xi)] ≤ i2T (1 − dation, grant number: PBEZP2-129524. E[Xi]) for all subsets T ⊆ [r]. Given that negative correlation is too strong a property This can be seen from a well-known pseudo-polynomial time to hold beyond matroid polytopes, it is natural to consider reduction of b-matchings to regular matchings (see [30]), and whether the property of concentration for linear functions the fact that our bounds are dimension free. Obtaining an can still be obtained via different means for other polytopes. efficient (that is, polynomial instead of pseudo-polynomial) An important special case to consider is the assignment poly- rounding procedure requires more work, and we defer the tope (equivalently the bipartite simple b-matching polytope). details. Arora, Frieze and Kaplan [1], motivated by applications to Our methods also yield related bounds that depend on approximation schemes for dense-graph optimization prob- n, and that work for negative coefficients. Our main applica- lems, considered this problem in a certain restricted setting tions in this paper, however, require dimension-free bounds and guaranteed concentration bounds in anp additive sense and we defer further discussion to a later version of the (with standard deviation on the order of n · poly(log n) paper. Our framework extends to polytopes/combinatorial and poly(log n), respectively, depending on whether the lin- structures that have certain exchange properties; Section 4 ear function under consideration has arbitrary coefficients or outlines this framework. non-negative coefficients). We discuss more details of their result and its relationship to our work later in the paper. It 1.1 Applications In this paper, we present two applica- is easy to show that even in the setting of the assignment tions, each of which applies to non-bipartite matchings and polytope, one cannot obtain concentration bounds while pre- matroid intersection. serving the expectation exactly. Multi-budgeted Optimization: There has been substantial In this paper we give a rounding scheme for two recent interest in the problem of optimizing linear (and also (classes of) polytopes, namely the polytope corresponding submodular functions) over a combinatorial structure with to the intersection of two matroids, and the non-bipartite additional linear/budget/packing constraints [5, 16, 22, 18, graph matching polytope. The rounding procedure for 2, 9]. Given a polynomial-time solvable problem, such as non-bipartite graph matching polytopes can easily be ap- maximum matching, can we optimize (in particular maxi- plied to simple b-matchings by a standard reduction from b- mize) over feasible solutions with an additional budget con- matchings to matchings [30]. Note that the assignment poly- straint of the form aT x ≤ b where a is a non-negative weight tope is a special case of both the intersection of two matroids vector? It is easy to see that the problem becomes NP- and simple b-matchings. Our main result is the following. hard via a reduction from knapsack. Randomized and deter- THEOREM 1.1. Let P be either a matroid intersection poly- ministic polynomial-time approximation schemes (PTASes) tope or a (non-bipartite graph) matching polytope. For any have been developed for important combinatorial optimiza- 1 tion problems subject to a single budget constraint: these fixed 0 < γ ≤ 2 , there is an efficient randomized rounding procedure, such that given a point x 2 P , it outputs a ran- include spanning trees [28], matchings and independent sets dom feasible solution R corresponding to a (integer) vertex in two matroids [5]. Two approaches have been used. of P such that E[1R] = (1 − γ)x. In addition, for any lin- Algebraic methods give a randomized pseudo- P polynomial time algorithm for the exact version of some ear function a(R) = i2R ai with ai 2 [0; 1], and for any " 2 [0; 1], we have: of these problems [8]; that is, given an integer B and an integer weight vector w, is there a solution S such that −µγ"2=20 • If µ ≤ E[a(R)], Pr[a(R) ≤ (1 − ")µ] ≤ e : w(S) = B? Such algorithms can be used to obtain a 2 randomized PTAS for any fixed number of budgets [16]. In • If µ ≥ E[a(R)], Pr[a(R) ≥ (1 + ")µ] ≤ e−µγ" =20: the case of matroids and matroid intersection, this approach For any t ≥ 2, is limited to representable (i.e. linear) matroids [8]. A different, deterministic approach is via the use of La- • If µ ≥ E[a(R)], Pr[a(R) ≥ tµ] ≤ e−µγ(2t−3)=20: grangian relaxation combined with various technical proper- ties of the underlying combinatorial structure.
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