Linear And Nonlinear Programming

2016

1,363 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Numerical Analysis

Citations in the corpus

• Boosting the Actor with Dual Critic (2017, methodology): “The augmented Lagrangian method (Boyd et al., 2010; Luenberger and Ye, 2015), also known as method of multipliers, is designed and widely used for such purposes.”
• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism (2020, background): “Finally, we remark that there are some similarities between SA-PINN training and penalty methods in optimization, which introduce a sequence of increasing penalty costs [26].”
• Multiple-instance discriminant analysis (2014, methodology): “Instead, we utilize the block coordinate ascent approach [36] to update the above two types of unknown variables alternatively and iteratively .”
• Human activity recognition using multi-features and multiple kernel learning (2014, methodology): “In the inner iteration a solver of SVM is implemented by fixing the vector of kernel combination weights while in the outer iteration a reduced gradient descent algorithm [42], along with the golden section search method [43], is used to update the combination weights with the fixed parameters of th”
• Second Order Optimality Conditions and Their Role in PDE Control (2015, methodology): “We will not address the associated second order conditions of the Karush-KuhnTucker theory and refer the reader to the textbooks [40, 61, 71].”
• Statistical Aspects of Wasserstein Distances (2018, methodology): “…structure of the constraints matrix allows the development of specialised algorithms: the Hungarian method of Kuhn (1955) and its variant by Munkres (1957) are classical examples, with alternatives such as network simplex, min flow-type algorithms and others (see Luenberger & Ye, 2008, Chapter 6).”
• Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future (2015, background): “is a huge need to model some problems in nonlinear programming approaches (Luenberger 2003).”
• One-class classification: taxonomy of study and review of techniques (2013, neutral): “(2002) propose a boosting-like OCC algorithm based on a technique called barrier optimization (Luenberger, 1984).”
• Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces (2013, positive): “Thus, xk can be found using Phase 1 of a linear program [28].”
• Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications (2017, methodology): “, the simplex algorithm (Luenberger and Ye, 2008) for general linear programs or the auction algorithm for network flow problems (Bertsekas, 1992, 2009) were found to scale rather poorly to very large problems such as image retrieval (Rubner et al.”
• An investigation of Newton-Sketch and subsampled Newton methods (2017, neutral): “If we let { λ T k 1 , λ T k 2 , . . . , λ T k d } denote the spectrum of the positive definite matrix ∇ 2 F T k ( w k ) , then the iterates { p rk } , r = 1 , 2 , . . . , d , generated by the CG method satisfy [13] We now prove Theorem 4.1 following the technique in Theorem 3.2 of [3].”
• HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation (2020, methodology): “For example, CDC[30], FABRIK[3] and IK-FA[55] have a low computational cost for each heuristic iteration.”