In this paper, a novel decentralized algorithm is proposed to minimize power flow loss in a large-scale future grid connecting with many real-time distributed generation systems by which power flows bi-directionally. The DC-power loss at each link is defined as the product of resistance and the square of current that can be considered as a quadratic flow cost. We employ the notion of tie-sets t...

The theoretical framework of Statistical Learning Theory (SLT) for pattern recognition problems is extended to comprehend the situations where an infinite value of the loss function is employed to prevent misclassifications in specific regions with high reliability. Sufficient conditions for ensuring the consistency of the Empirical Risk Minimization (ERM) criterion are then established and an ...

In this paper, we theoretically justify an approach popular among participants of the Higgs Boson Machine Learning Challenge to optimize approximate median significance (AMS). The approach is based on the following two-stage procedure. First, a real-valued function f is learned by minimizing a surrogate loss for binary classification, such as logistic loss, on the training sample. Then, given f...

We propose a novel algorithm for visual question answering based on a recurrent deep neural network, where every module in the network corresponds to a complete answering unit with attention mechanism by itself. The network is optimized by minimizing loss aggregated from all the units, which share model parameters while receiving different information to compute attention probability. For train...

We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict linear speedups, up to a problemdependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential f...

In this paper, a nonlinear loss minimization control strategy for an interior permanent magnet synchronous motor (IPMSM) based on a newly developed sliding mode approach is presented. This control method sets force the speed control of the IPMSM drives and simultaneously ensures the minimization of the losses besides the uncertainties exist in the system such as parameter variations which have ...

This work presents a strategy for minimizing active power losses in low-voltage grids, by coordinating the consumption of electric vehicles and power generation from solar panels. We show that minimizing losses, also reduces voltage variations, and illustrate how this may be employed for increasing the number of electric vehicles and photovoltaic systems in the grid without violating grid const...

Self-optimizing control provides nearly optimal operation of process systems in face of varying disturbances, where the inputs are adjusted to hold selected controlled variables (c) at constant setpoints. It is possible to have better self-optimizing properties by controlling linear combinations of measurements (c = Hy) than by controlling individual measurements. Previous work focussed on sele...