Given a metric (U, d) with |U | = n, a subset S ⊆ U with |S| = l, a non-negative function w : U → <, and an integer k ≤ l, the generalized static k-center and k-median problems ask to pick a subset X ⊆ S with |X| = k so as to minimize maxx∈U d(x,X) and ∑ x∈U d(x,X) · w(x), respectively, where d(x,X) = miny∈X d(x, y). Each point in X is called a center and each point in U is assigned to its clos...

This paper evaluates the performances of Perturbation Methods, the Parameterized Expectations Algorithm and Projection Methods in finding approximate decision rules of the basic neoclassical stochastic growth model. In contrast to the existing literature, we focus on comparing numerical methods for a given functional form of the approximate decision rules, and we repeat the evaluation for many ...

In this paper we extend the work of Smith and Papamichail (1999) and present fast approximate Bayesian algorithms for learn­ ing in complex scenarios where at any time frame, the relationships between explanatory state space variables can be described by a Bayesian network that evolve dynamically over time and the observations taken are not necessarily Gaussian. It uses recent devel­ opments in...

We present the first data structures that maintain near optimal maximum cardinality and maximum weighted matchings on sparse graphs in sublinear time per update. Our main result is a data structure that maintains a (1 + ǫ) approximation of maximum matching under edge insertions/deletions in worst case O( √ mǫ) time per update. This improves the 3/2 approximation given in [Neiman, Solomon, STOC ...

Dynamic Programming (DP) is known to be a standard optimization tool for solving Stochastic Optimal Control (SOC) problems, either over a finite or an infinite horizon of stages. Under very general assumptions, commonly employed numerical algorithms are based on approximations of the cost-to-go functions, by means of suitable parametric models built from a set of sampling points in the d-dimens...

There is a wide range of simulation problems that involve making decisions during the simulation, where we would like to make the best decisions possible, taking into account not only what we know when we make the decision, but also the impact of the decision on the future. Such problems can be formulated as dynamic programs, stochastic programs and optimal control problems, but these technique...

Spoken dialogue management strategy optimization by means of Reinforcement Learning (RL) is now part of the state of the art. Yet, there is still a clear mismatch between the complexity implied by the required naturalness of dialogue systems and the inability of standard RL algorithms to scale up. Another issue is the sparsity of the data available for training in the dialogue domain which can ...

Dynamic programming (DP) and reinforcement learning (RL) can be used to address problems from a variety of fields, including automatic control, artificial intelligence, operations research, and economy. Many problems in these fields are described by continuous variables, whereas DP and RL can find exact solutions only in the discrete case. Therefore, approximation is essential in practical DP a...

High taxi-out times (time between gate push-back and wheels off) at major airports is a primary cause for flight delays in the National Airspace System (NAS). These delays have a cascading effect and affect the performance of Air Traffic Control (ATC) System. Accurate prediction of taxi-out time is needed to make downstream schedule adjustments and better departure planning, which mitigates del...