convex minimization造句

例句与造句

1. Many optimization problems can be reformulated as convex minimization problems.
2. In mathematical optimization, Claude Lemar閏hal is known for his work in convex minimization.
3. He is a specialist in interior point methods, especially in convex minimization and linear programming.
4. With recent improvements in computing and in optimization theory, convex minimization is nearly as straightforward as linear programming.
5. For convex minimization problems with very large number of dimensions, subgradient-projection methods are suitable, because they require little storage.
6. It's difficult to find convex minimization in a sentence. 用convex minimization造句挺难的
7. In recent years, some interior-point methods have been suggested for convex minimization problems, but subgradient projection methods and related bundle methods of descent remain competitive.
8. Outside of combinatorial optimization, OM theory also appears in convex minimization in Rockafellar's theory of " monotropic programming " and related notions of " fortified descent ".
9. They are popularly used for non-differentiable convex minimization, where a convex objective function and its subgradient can be evaluated efficiently but usual gradient methods for differentiable optimization can not be used.
10. Ben-Israel's research into optimization included linear programming, a Newtonian bracketing method of convex minimization, input optimization, and risk modeling of dynamic programming, and the calculus of variations.
11. These equations are reduced to a series of convex minimization problems which are then solved with a combination of variable splitting and augmented Lagrangian ( FFT-based fast solver with a closed form solution ) methods.
12. These results are used by the theory of convex minimization along with geometric notions from functional analysis ( in Hilbert spaces ) such as the Hilbert projection theorem, the separating hyperplane theorem, and Farkas'lemma.
13. Convex minimization has applications in a wide range of disciplines, such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design, data analysis and modeling, statistics ( optimal design ), and finance.
14. The first important example of what you have in mind is maybe the use of the Legendre-Fenchel transformation in convex minimization; for instance it is the way one passes from the Lagrangian to the Hamiltonian formalism in mechanics .-- talk ) 06 : 56, 19 July 2009 ( UTC)