Results 11 to 20 of about 50,039 (279)

Penalized Parabolic Relaxation for Optimal Power Flow Problem [PDF]

2018 IEEE Conference on Decision and Control (CDC), 2018
This paper is concerned with optimal power flow (OPF), which is the problem of optimizing the transmission of electricity in power systems. Our main contributions are as follows: (i) we propose a novel parabolic relaxation, which transforms non-convex OPF problems into convex quadratically-constrained quadratic programs (QCQPs) and can serve as an ...
Zohrizadeh, Fariba, Kheirandishfard, Mohsen, Quarm, Edward, Madani, Ramtin   +3 more
openaire   +2 more sources

Screening for a Reweighted Penalized Conditional Gradient Method

Open Journal of Mathematical Optimization, 2022
The conditional gradient method (CGM) is widely used in large-scale sparse convex optimization, having a low per iteration computational cost for structured sparse regularizers and a greedy approach for collecting nonzeros.
Sun, Yifan, Bach, Francis
doaj   +1 more source

An Inertial Proximal-Gradient Penalization Scheme for Constrained Convex Optimization Problems [PDF]

Vietnam Journal of Mathematics, 2017
Proponemos un algoritmo de gradiente proximal con términos de penalización y efectos inerciales y de memoria para minimizar la suma de una función diferenciable adecuada, convexa e inferior y una función diferenciable convexa sujeta al conjunto de minimizadores de otra función diferenciable convexa.
Radu Ioan Boţ, Ernö Robert Csetnek, Nimit Nimana   +2 more
openaire   +2 more sources

A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network

Journal of Mathematics, 2020
Bridge regression is a special family of penalized regressions using a penalty function ∑Ajγ with γ≥1 that for γ=1 and γ=2, it concludes lasso and ridge regression, respectively. In case where the output variable in the regression model was imprecise, we
Delara Karbasi, Mohammad Reza Rabiei, Alireza Nazemi   +2 more
doaj   +1 more source

Exact Penalization and Necessary Optimality Conditions for Multiobjective Optimization Problems with Equilibrium Constraints [PDF]

Abstract and Applied Analysis, 2014
A calmness condition for a general multiobjective optimization problem with equilibrium constraints is proposed. Some exact penalization properties for two classes of multiobjective penalty problems are established and shown to be equivalent to the calmness condition.
Zhu, Shengkun, Li, Shengjie
openaire   +4 more sources

Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization

Risks, 2020
A regularization approach to model selection, within a generalized HJM framework, is introduced, which learns the closest arbitrage-free model to a prespecified factor model. This optimization problem is represented as the limit of a one-parameter family
Anastasis Kratsios, Cody Hyndman
doaj   +1 more source

Modified Courant-Beltrami penalty function and a duality gap for invex optimization problem

International Journal for Simulation and Multidisciplinary Design Optimization, 2019
In this paper, we modified a Courant-Beltrami penalty function method for constrained optimization problem to study a duality for convex nonlinear mathematical programming problems. Karush-Kuhn-Tucker (KKT) optimality conditions for the penalized problem
Hassan Mansur, Baharum Adam
doaj   +1 more source

An Improved Proximal Policy Optimization Method for Low-Level Control of a Quadrotor

Actuators, 2022
In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor.
Wentao Xue, Hangxing Wu, Hui Ye, Shuyi Shao   +3 more
doaj   +1 more source

Extended graphical lasso for multiple interaction networks for high dimensional omics data.

PLoS Computational Biology, 2021
There has been a spate of interest in association networks in biological and medical research, for example, genetic interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple ...
Yang Xu, Hongmei Jiang, Wenxin Jiang
doaj   +2 more sources

Sparse logistic principal components analysis for binary data [PDF]

, 2010
We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities ...
Hu, Jianhua, Huang, Jianhua Z., Lee, Seokho   +2 more
core   +3 more sources