This paper focuses on the fundamentals of discrete-time models using recursive utility. We examine the relation between preferences, utility, and aggregator, the existence of optimal paths, and several notions of impatience. In the one-sector model, we characterize optimal paths and derive a turnpike theorem. Topics beyond the scope of this paper include continuous time recursive utility, model...

Time discrete systems determined by systems of difference equations are considered. The characterizations of their optimal trajectories with given starting and final states is studied. An algorithm based on dynamic programming technique for determining such trajectories is proposed. In additional multicriterion version for considered control model is formulated and a general algorithm for deter...

We consider a user communicating over a fading channel with perfect channel state information. Data is assumed to arrive from some higher layer application and is stored in a buuer until it is transmitted. We study adapting the user's transmission rate and power based on the channel state information as well as the buuer occupancy; the objectives are to regulate both the long-term average trans...

In this paper, we propose SimSearch, an algorithm implementing a new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences. The initial phase of SimSearch is devoted to fulfil the binary similarity matrices by signalling the distances between occurrences of the same symbol. The scoring scheme is applied further, when an...

Market Clearing is an economic concept that features attractive properties when used for resource and task allocation, e.g., Pareto optimality and Envy Freeness. Recently, an algorithm based on Market Clearing, FMC_TA, has been shown to be most effective for realistic dynamic multi agent task allocation, outperforming general optimization methods, e.g., Simulated annealing, and dedicated algori...

Pareto optimization combines independent objectives by computing the Pareto front of the search space, yielding a set of optima where none scores better on all objectives than any other. Recently, it was shown that Pareto optimization seamlessly integrates with algebraic dynamic programming: when scoring schemes A and B can correctly evaluate the search space via dynamic programming, then so ca...

Dynamic data redistribution enhances data locality and improves algorithm performance for numerous scientific problems on distributed memory multi-computers systems. Regular data distribution typically employs BLOCK, CYCLIC, or BLOCK-CYCLIC(c) to specify array decomposition. Conversely, an irregular distribution specifies an uneven array distribution based on user-defined functions. Performing ...

Computer perception of biological motion is key to developing convenient and powerful humancomputer interfaces. Algorithms have been developed for tracking the body; however, initialization is done by hand. We propose a method for detecting a moving human body and for labeling its parts automatically in scenes that include extraneous motions and occlusion. We assume a Johansson display, i.e. th...

This paper studies the distribution of the classical t-ratio with data generated from distributions with no finite moments and shows how classical testing is affected by bimodality. A key condition in generating bimodality is independence of the observations in the underlying data generating process (DGP). The paper highlights the strikingly different implications of lack of correlation versus ...

S sampling problems arise in stochastic simulation and many other applications. Sampling is used to infer the unknown performance of several alternatives before one alternative is selected as best. This paper presents new economically motivated fully sequential sampling procedures to solve such problems, called economics of selection procedures. The optimal procedure is derived for comparing a ...