Results 61 to 70 of about 40,565 (244)

Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces [PDF]

The computation of robust regression estimates often relies on minimization of a convex functional on a convex set. In this paper we discuss a general technique for a large class of convex functionals to compute the minimizers iteratively which is ...
Bissantz, Nicolai, Dümbgen, Lutz, Munk, Axel, Stratmann, Bernd   +3 more
core  

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

Energy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan, Suresh Panchanathan, Chan Choon Kit, Elumalai Perumal Venkatesan, Nasim Hasan   +4 more
wiley   +1 more source

Joint Optimization of Radar and Jammer Space-time Cooperative Beamforming for a Multitasking Dynamic Scene

Leida xuebao
The modern radar confrontation situation is complex and changeable, and inter-system combat has become a basic feature. The overall system performance affects the initiative on the battlefield and even the final victory or defeat.
Xiaorong LIAO, Guohao SUN, Suchuan ZHONG, Xianxiang YU, Ming LI   +4 more
doaj   +1 more source

Approximate Convex Optimization by Online Game Playing

, 2006
Lagrangian relaxation and approximate optimization algorithms have received much attention in the last two decades. Typically, the running time of these methods to obtain a $\epsilon$ approximate solution is proportional to $\frac{1}{\epsilon^2 ...
Hazan, Elad
core   +2 more sources

Local Polynomial Regression and Filtering for a Versatile Mesh‐Free PDE Solver

International Journal for Numerical Methods in Fluids, EarlyView.
A high‐order, mesh‐free finite difference method for solving differential equations is presented. Both derivative approximation and scheme stabilisation is carried out by parametric or non‐parametric local polynomial regression, making the resulting numerical method accurate, simple and versatile. Numerous numerical benchmark tests are investigated for
Alberto M. Gambaruto
wiley   +1 more source

Sequential Convex Programming Methods for Solving Nonlinear Optimization Problems with DC constraints [PDF]

, 2011
This paper investigates the relation between sequential convex programming (SCP) as, e.g., defined in [24] and DC (difference of two convex functions) programming.
Diehl, Moritz, Quoc, Tran Dinh
core  

Convex Quadratic Sets and the Complexity of Mixed Integer Convex Quadratic Programming

SIAM Journal on Optimization
In pure integer linear programming it is often desirable to work with polyhedra that are full-dimensional, and it is well known that it is possible to reduce any polyhedron to a full-dimensional one in polynomial time. More precisely, using the Hermite normal form, it is possible to map a non full-dimensional polyhedron to a full-dimensional isomorphic
openaire   +3 more sources

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer, Kristofer Månsson, Pär Sjölander, Gazi Salah Uddin   +3 more
wiley   +1 more source

Algorithms for Convex Quadratic Programming

, 2014
The main contribution of this thesis is the development of a new algorithm for solving convex quadratic programs. It consists in combining the method of multipliers with an infeasible active-set method. Our approach is iterative. In each step we calculate an augmented Lagrange function.
openaire   +2 more sources

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source