Distributed learning in expert referral networks is a new Active Learning paradigm where experts—humans or automated agents—solve problems if they can or refer said problems to others with more appropriate expertise. Recent work augmented the basic learning-to-refer method with proactive skill posting, where experts may report their top skills to their colleagues, and proposed a modified algori...

In this paper ε-MDP-models are introduced and convergence theorems are proven using the generalized MDP framework of Szepesvári and Littman. Using this model family, we show that Q-learning is capable of finding near-optimal policies in varying environments. The potential of this new family of MDP models is illustrated via a reinforcement learning algorithm called event-learning which separates...

We prove a general result demonstrating the power of Lagrangian relaxation in solving constrained maximization problems with arbitrary objective functions. This yields a unified approach for solving a wide class of subset selection problems with linear constraints. Given a problem in this class and some small ε ∈ (0, 1), we show that if there exists a ρ-approximation algorithm for the Lagrangia...

Although it is known that correct dynamical equations of motion for a nonholonomic system cannot be obtained from a Lagrangean that has been augmented with a sum of the nonholonomic constraint equations weighted with multipliers, previous publications suggest otherwise. An example has been proposed in support of augmentation and purportedly demonstrates that an accepted method fails to produce ...

We investigate the optimal portfolio deleveraging (OPD) problem with permanent and temporary price impacts, where objective is to maximize equity while meeting a prescribed debt/equity requirement. take real situation cross impact among different assets into consideration. The resulting is, however, nonconvex quadratic program constraint box constraint, which known be NP-hard. In this paper, we...

We consider maximizing a monotone submodular function under cardinality constraint or knapsack in the streaming setting. In particular, elements arrive sequentially and at any point of time, algorithm has access to only small fraction data stored primary memory. propose following algorithms taking O(ε− 1) passes: (1) (1 − e− 1 ε)-approximation for cardinality-constrained problem, (2) (0.5 knaps...

We prove a general result demonstrating the power of Lagrangian relaxation in solving constrained maximization problems with arbitrary objective functions. This yields a unified approach for solving a wide class of subset selection problems with linear constraints. Given a problem in this class and some small ε ∈ (0, 1), we show that if there exists an r-approximation algorithm for the Lagrangi...

This paper is concerned with the numerical approximation of the isothermal Euler equations for charged particles subject to the Lorentz force (the ’Euler-Lorentz’ system). When the magnetic field is large, or equivalently, when the parameter ε representing the non-dimensional ion cyclotron frequency tends to zero, the so-called drift-fluid (or gyrofluid) approximation is obtained. In this limit...

This analysis investigates the distortion of flow measurement and the increase of cavitation along orifice flowmeter. The analysis using the numerical method (CFD) validated the distortion of flow measurement through the inlet velocity profile considering the convergence and grid dependency. Realizable k-e model was selected and y+ was about 50 in this numerical analysis. This analysis also est...

This paper presents a self-adaptive differential evolution with augmented Lagrange multiplier method (SADE_ALM) for solving optimal power flow (OPF) problems with non-smooth generator fuel cost curves. The SADE_ALM is a modified version of conventional differential evolution (DE) by integrating mutation factor (F ) and crossover constant (CR ) as additional control variables. An augmented Lagra...