Results 1 to 10 of about 7,146 (115)
An end-to-end data-driven optimization framework for constrained trajectories
Data-Centric Engineering, 2022 Many real-world problems require to optimize trajectories under constraints. Classical approaches are often based on optimal control methods but require an exact knowledge of the underlying dynamics and constraints, which could be challenging or even out
Florent Dewez +3 more
doaj +1 more source
A new logarithmic penalty function approach for nonlinear constrained optimization problem [PDF]
Decision Science Letters, 2019 This paper presents a new penalty function called logarithmic penalty function (LPF) and examines the convergence of the proposed LPF method. Furthermore, the LaGrange multiplier for equality constrained optimization is derived based on the first-order ...
Mansur Hassan , Adam Baharum
doaj +1 more source
Novel multi-objective topology optimization method for stiffness and stress of continuum structures
Advances in Mechanical Engineering, 2022 In this paper, a topology optimization method combining Bi-directional Evolutionary Structural Optimization (BESO) and Fast Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed, which is called BESO-NSGA-II.
Shuo Feng +4 more
doaj +1 more source
IEEE Access, 2021 In this paper, a practical model predictive control (MPC) for tracking desired reference trajectories is demonstrated for controlling a class of nonlinear systems subject to constraints, which comprises diverse mechanical applications.
Hossam S. Abbas +4 more
doaj +1 more source
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
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 +2 more
doaj +1 more source
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 +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