Quadratic knapsack problem (QKP) has a central role in integer and combinatorial optimization, while efficient algorithms to general QKPs are currently very limited. We present an approximate dynamic programming (ADP) approach for solving convex QKPs where variables may take any integer value and all coefficients are real numbers. We approximate the function value using (a) continuous quadratic...

We consider the problem of generating optimal stochastic policies for Constrained Stochastic Shortest Path problems, which are a natural model for planning under uncertainty for resourcebounded agents with multiple competing objectives. While unconstrained SSPs enjoy a multitude of efficient heuristic search solution methods with the ability to focus on promising areas reachable from the initia...

This paper describes Soft Computing approach to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The proposed “intelligent” control systems that are based on a combination of fuzzy logic (or neural networks) and mathematical programming (or heuristic) techniques make “on line” control decisions of the highest quality. In the first step of the ...

Stream programming is a paradigm where a program is structured by a set of computational nodes connected by streams. Focusing on data moving between computational nodes via streams, this programming model fits well for applications that process long sequences of data. We call such applications reactive stream programs (RSPs) to distinguish them from stream programs with rather small and finite ...

As a mechanism to support group communications, multicasting faces a serious state scalability problem when there are large numbers of groups in the network: lots of resources (e.g., memory to maintain group state information) and control overhead (e.g., multicast tree setup and maintenance) are required to manage the groups. Recently, an efficient solution called aggregated multicast is propos...