Results 91 to 100 of about 266 (177)
The concepts of (Φ, ρ)-invexity have been given by Carsiti,Ferrara and Stefanescu[32]. We consider a second-order dual model associated to a multiobjective programming problem involving support functions and a weak duality result is established under ...
Bandana Priya, Ganesh Kumar Thakur
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Tensor methods for finding approximate stationary points of convex functions. [PDF]
Grapiglia GN, Nesterov Y.
europepmc +1 more source
Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method. [PDF]
Doikov N, Nesterov Y.
europepmc +1 more source
On due-date based valid cuts for the sequential ordering problem
Integer Programming, Lagrangean relaxation, Hamiltonian Path, Valid Cuts, Tightening, 90C10, 90C27, 90C30,
L. Escudero, M. Ortuño
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Sufficient Second Order Optimality Conditions for C^1 Multiobjective Optimization Problems [PDF]
2000 Mathematics Subject Classification: Primary 90C29; Secondary 90C30.In this work, we use the notion of Approximate Hessian introduced by Jeyakumar and Luc [19], and a special scalarization to establish sufficient optimality conditions for constrained
Gadhi, N.
core
Pseudomonotone operators and the Bregman Proximal Point Algorithm
Pseudomonotone operators, Variational inequalities, Bregman distances, Proximal Point algorithm, Interior-point-effect, 47J20, 65J20, 65K10, 90C26, 90C30,
Nils Langenberg
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Quadratic nonconvex optimization, Regularized Lagrangian, Strong duality, Quadratic constraints, Linear equality constraints, Trust-region problems, Alternative theorems, 90C26, 90C46, 90C20, 90C30,
V. Jeyakumar, Guoyin Li
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Second order optimality conditions for bilevel set optimization problems
Approximate Jacobian, Approximate Hessian, Recession matrices, Bilevel optimization, Second order approximation, Necessary optimality conditions, Regularity condition, Set valued mappings, Support function, 90C29, 49J52, 90C30, 49K99,
N. Gadhi, S. Dempe
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Navigating large-scale unconstrained optimization complexities requires a synergistic integration of the low memory footprint of conjugate gradient (CG) methods with the fast convergence of quasi-Newton (QN) methods.
M. Salimi Khorshidi, N. Bidabadi
doaj +1 more source
Convexification for data fitting
Convexification, Global optimization, Local minima, Data fitting, Neural network, Nonlinear regression, Minimax, Robustifying error crition, Degree of robustness, 90C30, 90C31, 62M45, 62G08,
James Lo
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