Suchergebnisse - Difference of convex/nonconvex functions algorithm

Iterative Reweighted Minimization for Generalized Norm/Quasi-Norm Difference Regularized Unconstrained Nonlinear Programming

Autoren: Yi Cen Linna Zhang Ke Wang et al.

Quelle: IEEE Access, Vol 7, Pp 153102-153122 (2019)

Schlagwörter: http://www.niso.org/schemas/ali/1.0/' xmlns:mml='http://www.w3.org/1998/Math/MathML' xmlns:xlink='http://www.w3.org/1999/xlink' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'>lₚ–http://www.niso.org/schemas/ali/1.0/' xmlns:mml='http://www.w3.org/1998/Math/MathML' xmlns:xlink='http://www.w3.org/1999/xlink' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'>q-null space property, iterative reweighted http://www.niso.org/schemas/ali/1.0/' xmlns:mml='http://www.w3.org/1998/Math/MathML' xmlns:xlink='http://www.w3.org/1999/xlink' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'>lₜ minimization, http://www.niso.org/schemas/ali/1.0/' xmlns:mml='http://www.w3.org/1998/Math/MathML' xmlns:xlink='http://www.w3.org/1999/xlink' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'>lₚ–http://www.niso.org/schemas/ali/1.0/' xmlns:mml='http://www.w3.org/1998/Math/MathML' xmlns:xlink='http://www.w3.org/1999/xlink' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'>q-minimization, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences, Difference of convex/nonconvex functions algorithm, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08877712.pdf
https://doaj.org/article/808b2f083c874bd2b9005fae376a7254
https://dblp.uni-trier.de/db/journals/access/access7.html#CenZWC19
https://ieeexplore.ieee.org/document/8877712/

Nested Bregman Iterations for Decomposition Problems.

Autoren: Wolf, Tobias Driggs, Derek T. Papafitsoros, Kostas et al.

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 3, p1963-2007, 45p

Efficient approaches for ℓ - ℓ regularization and applications to feature selection in SVM.

Autoren: Le Thi, Hoai Pham Dinh, Tao Thiao, Mamadou

Quelle: Applied Intelligence; Sep2016, Vol. 45 Issue 2, p549-565, 17p

Schlagwörter: SUPPORT vector machines, MATHEMATICAL regularization, FEATURE selection, CONVEX functions, APPROXIMATION algorithms

A Systematic Approach to General Higher-Order Majorization-Minimization Algorithms for (Non)convex Optimization.

Autoren: Necoara, Ion Lupu, Daniela

Quelle: SIAM Journal on Optimization; 2025, Vol. 35 Issue 4, p2372-2401, 30p

Schlagwörter: MATHEMATICAL optimization, OPTIMIZATION algorithms, APPROXIMATION theory, LIPSCHITZ continuity, NONCONVEX programming, SURROGATE-based optimization

Cost Optimization in a GI / M /2/ N Queue with Heterogeneous Servers, Working Vacations, and Impatient Customers via the Bat Algorithm.

Autoren: Guendouzi, Abdelhak Bouzebda, Salim

Quelle: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 21, p3559, 34p

Schlagwörter: QUEUING theory, OPTIMIZATION algorithms, NUMERICAL analysis, COMPUTER performance, COST functions

Efficient Reduction of Variances in Stochastic Spectral Conjugate Gradient Algorithm.

Autoren: YIMER, SEIFU ENDRIS KUMAM, POOM CHAIPUNYA, PARIN

Quelle: Carpathian Journal of Mathematics; 2025, Vol. 41 Issue 3, p849-862, 14p

Schlagwörter: CONVEX functions, CONJUGATE gradient methods, NONLINEAR equations, MACHINE learning, ALGORITHMS, GENERALIZATION

Formalization of Convergence Rates of Four First-order Algorithms for Convex Optimization.

Autoren: Li, Chenyi Wang, Ziyu He, Wanyi et al.

Quelle: Journal of Automated Reasoning; Dec2025, Vol. 69 Issue 4, p1-25, 25p

Fast and Convergent Method for Large-Scale Sensor Network Localization With Anchor Uncertainty.

Autoren: Gur, Eyal Sabach, Shoham

Quelle: ACM Transactions on Sensor Networks; Sep2025, Vol. 21 Issue 5, p1-30, 30p

Schlagwörter: SENSOR networks, LOCALIZATION theory, DISTRIBUTED computing, EUCLIDEAN distance, ALGORITHMS, MAXIMUM likelihood statistics

The Descent–Ascent Algorithm for DC Programming.

Autoren: D'Alessandro, Pietro Gaudioso, Manlio Giallombardo, Giovanni et al.

Quelle: INFORMS Journal on Computing; Mar/Apr2024, Vol. 36 Issue 2, p657-671, 15p

Schlagwörter: DATA libraries, ALGORITHMS, NONSMOOTH optimization, CONVEX functions

Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods.

Autoren: Dvurechensky, Pavel Ostroukhov, Petr Gasnikov, Alexander et al.

Quelle: Optimization Methods & Software; Oct2024, Vol. 39 Issue 5, p1068-1103, 36p

Schlagwörter: CONVEX functions, LOGISTIC regression analysis, PROBLEM solving, ALGORITHMS, MOTIVATION (Psychology)

A Lagrange-Based Multi-Objective Framework for Wind–Thermal Economic Emission Dispatch.

Autoren: Mbangeni, Litha Krishnamurthy, Senthil

Quelle: Processes; Sep2025, Vol. 13 Issue 9, p2814, 29p

Schlagwörter: WIND power, FUEL costs, OPTIMIZATION algorithms, RESOURCE allocation, SUSTAINABILITY, LAGRANGE problem, GREENHOUSE gas mitigation, MULTI-objective optimization

Efficiency of Stochastic Coordinate Proximal Gradient Methods on Nonseparable Composite Optimization.

Autoren: Necoara, Ion Chorobura, Flavia

Quelle: Mathematics of Operations Research; May2025, Vol. 50 Issue 2, p993-1018, 26p

Schlagwörter: STOCHASTIC analysis, CONVEX sets, SCALABILITY, ALGORITHMS, GRANTS (Money)

An Efficient Optimization Model and Tabu Search–Based Global Optimization Approach for the Continuous p -Dispersion Problem.

Autoren: Lai, Xiangjing Lin, Zhenheng Hao, Jin-Kao et al.

Quelle: INFORMS Journal on Computing; Jul/Aug2025, Vol. 37 Issue 4, p1018-1043, 26p

Schlagwörter: MATHEMATICAL optimization, TABU search algorithm, MATHEMATICAL programming, NP-hard problems, GLOBAL optimization, RESOURCE allocation

Tensor Completion via Smooth Rank Function Low-Rank Approximate Regularization.

Autoren: Yu, Shicheng Miao, Jiaqing Li, Guibing et al.

Quelle: Remote Sensing; Aug2023, Vol. 15 Issue 15, p3862, 23p

Schlagwörter: SMOOTHNESS of functions, REMOTE sensing, MATHEMATICAL regularization, EXPECTATION-maximization algorithms

Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm.

Autoren: Pham Dinh, Tao Le Thi, Hoai Le, Hoai

Quelle: Machine Learning; Oct2015, Vol. 101 Issue 1-3, p163-186, 24p

Schlagwörter: FEATURE selection, MATHEMATICAL regularization, CONVEX functions, CONVEX programming, NONCONVEX programming, SUPPORT vector machines, APPROXIMATION algorithms, MATHEMATICAL models

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms.

Autoren: Goujon, Alexis Neumayer, Sebastian Unser, Michael

Quelle: SIAM Journal on Imaging Sciences; 2024, Vol. 17 Issue 1, p91-115, 25p

Difference of convex functions algorithms (DCA) for image restoration via a Markov random field model.

Autoren: Le Thi, Hoai Pham Dinh, Tao

Quelle: Optimization & Engineering; Dec2017, Vol. 18 Issue 4, p873-906, 34p

Convex Predictor–Nonconvex Corrector Optimization Strategy with Application to Signal Decomposition.

Autoren: Girometti, Laura Huska, Martin Lanza, Alessandro et al.

Quelle: Journal of Optimization Theory & Applications; Sep2024, Vol. 202 Issue 3, p1286-1325, 40p

Schlagwörter: COST functions, CROSS correlation, SIGNALS & signaling, NOISE, ALGORITHMS

A zeroth-order stochastic implicit method for bilevel-structured actor-critic schemes.

Autoren: Tao, Haochen Cui, Shisheng Li, Zhuo et al.

Quelle: SCIENCE CHINA Information Sciences; May2025, Vol. 68 Issue 5, p1-19, 19p

Advancing Image Reconstruction in Diffuse Optical Tomography: An Overview of Regularization Methods.

Autoren: Siddalingaiah, Harish G. Jagannath, Ravi Prasad K. Prashanth, Gurusiddappa R.

Quelle: Sensing & Imaging; 5/14/2025, Vol. 26 Issue 1, p1-49, 49p