Results 291 to 300 of about 487,508 (331)
Combining surface drifters and high resolution global simulations enables the mapping of internal tide surface energy. [PDF]
Caspar-Cohen Z +7 more
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Continuous and Autonomous Monitoring of Changes in Left Ventricular dP/dt<sub>max</sub> Using an Epicardial Accelerometer. [PDF]
Frostelid VC +8 more
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Cavity expansion theory with state-dependent mohr-coulomb model and its application to cone penetration tests. [PDF]
Li B +5 more
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The baryon-baryon interaction in the large- N c limit. [PDF]
Vonk T, Meißner UG.
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An importance sampling vortex particle method for turbulence visualization simulation in maritime simulators. [PDF]
Zhu T, Ren H, Tao R, Wei D, Xie P.
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Some Lagrangians for systems without a Lagrangian
Physica Scripta, 2011We demonstrate how to construct many different Lagrangians for two famous examples that were deemed by Douglas (1941 Trans. Am. Math. Soc. 50 71–128) not to have a Lagrangian. Following Bateman's dictum (1931 Phys. Rev. 38 815–9), we determine different sets of equations that are compatible with those of Douglas and derivable from a variational ...
M.C. Nucci, P.G.L. Leach
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Management Science, 1970
There is a dual program linked with every nonlinear program. The dual objective function is called the Lagrangian; it is defined in terms of the original problem. This note presents a characterization of the Lagrangian subgradients under general conditions. The theorem follows from a result of Danskin [1] that can be used (see [2]) to characterize the
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There is a dual program linked with every nonlinear program. The dual objective function is called the Lagrangian; it is defined in terms of the original problem. This note presents a characterization of the Lagrangian subgradients under general conditions. The theorem follows from a result of Danskin [1] that can be used (see [2]) to characterize the
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Lagrangian-Perfect Hypergraphs
Annals of Combinatorics, 2023For an \(r\)-graph \((r\ge2)\) \(G=(V,E)\) with \(V=[n]\), and \(\vec x=(x_1,\dots,x_n)\in[0,\infty)^n\), \(\lambda(G,\vec x)=\sum\limits_{e\in E}\prod\limits_{i\in e}x_i\); the Lagrangian is \(\lambda(G) =\max\{\lambda(G,\vec x):\vec x\in\Delta\}\), where \(\Delta=\{\vec x=(x_1,x_2,\dots,x_n)\in[0,1]^n:x_1+x_2+\dots+x_n=1\}\); the Lagrangian density \(
Yan, Zilong, Peng, Yuejian
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