Rani Izsak

Welfare maximization and the supermodular degree

Given a set of items and a collection of players, each with a nonnegative monotone valuation set function over the items, the welfare maximization problem requires that every item be allocated to exactly one player, and one wishes to maximize the sum of values obtained by the players, as computed by applying the respective valuation function to the bundle of items allocated to the player. This problem in its full generality is NP-hard, and moreover, at least as hard to approximate as set-packing. Better approximation guarantees are known for restricted classes of valuation functions. In this work we introduce a new parameter, the supermodular degree of a valuation function, which is a measure for the extent to which the function exhibits supermodular behavior. We design an approximation algorithm for the welfare maximization problem whose approximation guarantee is linear in the supermodular degree of the underlying valuation functions.

Constrained Monotone Function Maximization and the Supermodular Degree

The problem of maximizing a constrained monotone set function has many practical applications and generalizes many combinatorial problems. Unfortunately, it is generally not possible to maximize a monotone set function up to an acceptable approximation ratio, even subject to simple constraints. One highly studied approach to cope with this hardness is to restrict the set function. An outstanding disadvantage of imposing such a restriction on the set function is that no result is implied for set functions deviating from the restriction, even slightly. A more flexible approach, studied by Feige and Izsak, is to design an approximation algorithm whose approximation ratio depends on the complexity of the instance, as measured by some complexity measure. Specifically, they introduced a complexity measure called supermodular degree, measuring deviation from submodularity, and designed an algorithm for the welfare maximization problem with an approximation ratio that depends on this measure. In this work, we give the first (to the best of our knowledge) algorithm for maximizing an arbitrary monotone set function, subject to a k-extendible system. This class of constraints captures, for example, the intersection of k-matroids (note that a single matroid constraint is sufficient to capture the welfare maximization problem). Our approximation ratio deteriorates gracefully with the complexity of the set function and k. Our work can be seen as generalizing both the classic result of Fisher, Nemhauser and Wolsey, for maximizing a submodular set function subject to a k-extendible system, and the result of Feige and Izsak for the welfare maximization problem. Moreover, when our algorithm is applied to each one of these simpler cases, it obtains the same approximation ratio as of the respective original work.

Building a good team: secretary problems and the supermodular degree

In the Secretary Problem, one has to hire the best among n candidates. The candidates are interviewed, one at a time, at a random order, and one has to decide on the spot, whether to hire a candidate or continue interviewing. It is well known that the best candidate can be hired with a probability of 1/e (Dynkin, 1963). Recent works extend this problem to settings in which multiple candidates can be hired, subject to some constraint. Here, one wishes to hire a set of candidates maximizing a given set function. Almost all extensions considered in the literature assume the objective set function is either linear or submodular. Unfortunately, real world functions might not have either of these properties. Consider, for example, a scenario where one hires researchers for a project. Indeed, it can be that some researchers can substitute others for that matter. However, it can also be that some combinations of researchers result in synergy (see, e.g, Woolley et al., Science 2010, for a research about collective intelligence). The first phenomenon can be modeled by a submoudlar set function, while the latter cannot. In this work, we study the secretary problem with an arbitrary non-negative monotone function, subject to a general matroid constraint. It is not difficult to prove that, generally, only very poor results can be obtained for this class of objective functions. We tackle this hardness by combining the following: 1.Parametrizing our algorithms by the supermodular degree of the objective function (defined by Feige and Izsak, ITCS 2013), which, roughly speaking, measures the distance of a function from being submodular. 2.Suggesting an (arguably) natural model that permits approximation guarantees that are polynomial in the supermodular degree (as opposed to the standard model which allows only exponential guarantees).

Monotone Circuits: One-Way Functions versus Pseudorandom Generators

We study the computability of one-way functions and pseudorandom generators by monotone circuits, showing a substantial gap between the two: On one hand, there exist one-way functions that are computable by (uniform) polynomial-size monotone functions, provided (of course) that one-way functions exist at all. On the other hand, no monotone function can be a pseudorandom generator.

Working Together: Committee Selection and the Supermodular Degree

We introduce a voting rule for committee selection that captures positive correlation (synergy) between candidates. We argue that positive correlation can naturally happen in common scenarios that are related to committee selection. For example, in the movies selection problem, where prospective travelers are requested to choose the movies that will be available on their flight, it is reasonable to assume that they will tend to prefer voting for a movie in a series, only if they can watch also the former movies in that series. In elections to the parliament, it can be that two candidates are working extremely well together, so voters will benefit from being represented by both of them together. In our model, the preferences of the candidates are represented by set functions, and we would like to maximize the total satisfaction of the voters. We show that although computing the best solution is NP-hard, there exists an approximation algorithm with approximation guarantees that deteriorate gracefully with the amount of synergy between the candidates, as measured by an extension of the supermodular degree [Feige and Izsak, ITCS 2013] that we introduce -- the \textsf{joint supermodular degree}.