Results 21 to 30 of about 43,906 (221)
On global minimizers of quadratic functions with cubic regularization [PDF]
, 2018 In this paper, we analyze some theoretical properties of the problem of minimizing a quadratic function with a cubic regularization term, arising in many methods for unconstrained and constrained optimization that have been proposed in the last years ...
Cristofari, Andrea +2 more
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Flight Gate Assignment with a Quantum Annealer [PDF]
, 2018 Optimal flight gate assignment is a highly relevant optimization problem from airport management. Among others, an important goal is the minimization of the total transit time of the passengers.
A Haghani +6 more
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Unconstrained formulation of standard quadratic optimization problems [PDF]
TOP, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Immanuel M. Bomze +2 more
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Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization
Scientific Reports, 2022 Recently, inspired by quantum annealing, many solvers specialized for unconstrained binary quadratic programming problems have been developed. For further improvement and application of these solvers, it is important to clarify the differences in their ...
Hiroki Oshiyama, Masayuki Ohzeki
doaj +1 more source
Discrete Dynamics in Nature and Society, 2017 We propose a new method for equality constrained optimization based on augmented Lagrangian method. We construct an unconstrained subproblem by adding an adaptive quadratic term to the quadratic model of augmented Lagrangian function.
Hao Zhang, Qin Ni
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An Improved Adaptive Trust-Region Method for Unconstrained Optimization
Mathematical Modelling and Analysis, 2014 In this study, we propose a trust-region-based procedure to solve unconstrained optimization problems that take advantage of the nonmonotone technique to introduce an efficient adaptive radius strategy.
Hamid Esmaeili, Morteza Kimiaei
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An Algorithm for Unconstrained Quadratically Penalized Convex Optimization [PDF]
Communications in Statistics - Simulation and Computation, 2011 A descent algorithm, "Quasi-Quadratic Minimization with Memory" (QQMM), is proposed for unconstrained minimization of the sum, $F$, of a non-negative convex function, $V$, and a quadratic form. Such problems come up in regularized estimation in machine learning and statistics. In addition to values of $F$, QQMM requires the (sub)gradient of $V$.
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Robust optimisation of unconstrained binary quadratic problems
International Journal of Operational Research, 2019 In this paper we focus on the unconstrained binary quadratic optimization model, maximize x^t Qx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix.. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and ...
Mark W. Lewis +2 more
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On the Complexity of Local Search in Unconstrained Quadratic Binary Optimization [PDF]
SIAM Journal on Optimization, 2016 Minor update in 2016: simplified ...
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A Modified Class of Conjugate Gradient Algorithms Based on Quadratic Model for Nonlinear Unconstrained Optimization [PDF]
Al-Rafidain Journal of Computer Sciences and Mathematics, 2014 In this paper, we have investigated a new class of conjugate gradient algorithms for unconstrained non-linear optimization which are based on the quadratic model. Some theoretical results are investigated which are sufficient descent and ensure the local
Basim Hassan, Hameed Sadiq
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