Solving Large Scale Classification Problems with Stochastic Based Optimization
نویسندگان
چکیده
منابع مشابه
Solving Large Scale Optimization Problems via Grid and Cluster Computing∗
Solving large scale optimization problems requires a huge amount of computational power. The size of optimization problems that can be solved on a few CPUs has been limited due to a lack of computational power. Grid and cluster computing has received much attention as a powerful and inexpensive way of solving large scale optimization problems that an existing single CPU cannot process. The aim ...
متن کاملA Survey on Metaheuristics for Solving Large Scale Optimization Problems
In recent years, there has been a remarkable improvement in the computing power of computers. As a result, numerous realworld optimization problems in science and engineering, possessing very high dimensions, have appeared. In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. ...
متن کاملSolving large-scale optimization problems related to Bell's Theorem
Impossibility of finding local realistic models for quantum correlations due to entanglement is an important fact in foundations of quantum physics, gaining now new applications in quantum information theory. We present an in-depth description of a method of testing the existence of such models, which involves two levels of optimization: a higher-level non-linear task and a lower-level linear p...
متن کاملSolving Large Scale Optimization Problems by Opposition-Based Differential Evolution (ODE)
This work investigates the performance of Differential Evolution (DE) and its opposition-based version (ODE) on large scale optimization problems. Opposition-based differential evolution (ODE) has been proposed based on DE; it employs opposition-based population initialization and generation jumping to accelerate convergence speed. ODE shows promising results in terms of convergence rate, robus...
متن کاملSolving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities
We study a complex class of stochastic programming problems involving a joint chance constraint with random technology matrix and stochastic quadratic inequalities. We present a basic mixedinteger nonlinear reformulation based on Boolean modeling and derive several variants of it. We present detailed empirical results comparing the various reformulations and several easy to implement algorithmi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2020
ISSN: 1877-0509
DOI: 10.1016/j.procs.2020.02.247