Salman Fadaei

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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. Our algorithms combine existing local search and greedy based algorithms. Different constraints that we study are exact cardinality and multiple(More)
We propose a truthful-in-expectation, (1 − 1 e )-approximation mechanism for the generalized assignment auction. In such an auction, each bidder has a knapsack valuation function and bidders’ values for items are private. We present a novel convex optimization program for the auction which results in a maximal-in-distributional-range (MIDR) allocation rule.(More)
In this paper, we study a mechanism design problem for a strategic variant of the generalized assignment problem (GAP) in a both payment-free and priorfree environment. In GAP, a set of items has to be optimally assigned to a set of bins without exceeding the capacity of any singular bin. In the strategic variant of the problem we study, bins are held by(More)
Approximating the optimal social welfare while preserving truthfulness is a well studied problem in algorithmic mechanism design. Assuming that the social welfare of a given mechanism design problem can be optimized by an integer program whose integrality gap is at most α, Lavi and Swamy [1] propose a general approach to designing a randomized(More)
In random allocation rules, typically first an optimal fractional point is calculated via solving a linear program. Lying in the feasible region of the linear program, the fractional point satisfies the underlying constraints. In effect, the point represents a fractional assignment of objects or more generally packages of objects to agents. In order to(More)