688 research outputs found
Ex-Post Optimal Knapsack Procurement
We consider a budget-constrained mechanism designer who selects an optimal set of projects to maximize her utility. Projects may differ in their value for the designer, and their cost is private information. In this allocation problem, the quantity of procured projects is endogenously determined by the mechanism. The designer faces ex-post constraints: The participation and budget constraints must hold for each possible outcome, while the mechanism must be strategyproof. We identify settings in which the class of optimal mechanisms has a deferred acceptance auction representation which allows an implementation with a descending-clock auction. Only in the case of symmetric projects do price clocks descend synchronously such that the cheapest projects are implemented. The case in which values or costs are asymmetrically distributed features a novel tradeoff between quantity and quality. The reason is that guaranteeing allocation to the most favorable projects under strategyproofness comes at the cost of a diminished expected number of conducted projects
Designing a Frequency Selective Scheduler for WiMAX using Genetic Algorithms
Projecte final de carrera fet en col.laboració amb University of Stuttgar
Constraint Propagation on GPU: A Case Study for the Bin Packing Constraint
The Bin Packing Problem is one of the most important problems in discrete
optimization, as it captures the requirements of many real-world problems.
Because of its importance, it has been approached with the main theoretical and
practical tools. Resolution approaches based on Linear Programming are the most
effective, while Constraint Programming proves valuable when the Bin Packing
Problem is a component of a larger problem. This work focuses on the Bin
Packing constraint and explores how GPUs can be used to enhance its propagation
algorithm. Two approaches are motivated and discussed, one based on knapsack
reasoning and one using alternative lower bounds. The implementations are
evaluated in comparison with state-of-the-art approaches on different
benchmarks from the literature. The results indicate that the GPU-accelerated
lower bounds offers a desirable alternative to tackle large instances
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