This paper looks at the problem of reducing energy use robot movements in a station with stochastic execution times, while keeping productivity station. The is formulated as optimization problem, that constrains makespan to meet deadline high probability. function times operations, and goal reduce this by finding optimal operation order. A theoretical motivation why variables under some conditi...

LISA Pathfinder’s measurement of a relative acceleration noise between two free-falling test masses with a square root of the power spectral density of 5.2± 0.1 fm s/ √ Hz [1] appreciably constrains collapse models. In particular, we bound the localization rate parameter, λCSL, in the continuous spontaneous localization model (CSL) to be at most (2.96 ± 0.12)× 10 s. Moreover, we bound the regul...

Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and proximal stochastic coordinate descent (ProxSCD) have been widely used to so...

Optimized task scheduling is one of the most important challenges in multiprocessor environments such as parallel and distributed systems. In such these systems, each parallel program is decomposed into the smaller segments so-called tasks. Task execution times, precedence constrains and communication costs are modeled by using a directed acyclic graph (DAG) named task graph. The goal is to min...

This paper investigates foliation structures of configuration manifolds and develops integrating algorithms for a class of constraints that contain the time variable, called A-rheonomous affine constrains. We first present some preliminaries on the A-rheonomous affine constrains. Next, theoretical analysis on foliation structures of configuration manifolds is done for the respective three cases...