例句與造句

1. Many interesting problems can be formulated as convex optimization problems of form
2. The proposed algorithm solves N convex optimization problems in parallel based on exchange of information among controllers.
3. This is a linear matrix inequality ( LMI ) feasibility test, which is a convex optimization problem.
4. "' Bregman's method "'is an iterative algorithm to solve certain convex optimization problems.
5. They are iterative algorithms that eventually converge to the optimal solution of the convex optimization problem that is associated with these information theoretic concepts.
6. It's difficult to find convex optimization problem in a sentence. 用convex optimization problem造句挺難的
7. "' Proximal gradient methods "'are a generalized form of projection used to solve non-differentiable convex optimization problems.
8. "' Conic optimization "'is a subfield of convex optimization that studies a class of structured convex optimization problems called conic optimization problems.
9. A variety of one-dimensional FIR filter design problems can be expressed as convex optimization problems over real trigonometric polynomials, subject to spectral mask constraints.
10. Such procedures are commonly used to find integer solutions to mixed integer linear programming ( MILP ) problems, as well as to solve general, not necessarily differentiable convex optimization problems.
11. The class of conic optimization problems is a subclass of convex optimization problems and it includes some of the most well known classes of convex optimization problems, namely linear and semidefinite programming.
12. The class of conic optimization problems is a subclass of convex optimization problems and it includes some of the most well known classes of convex optimization problems, namely linear and semidefinite programming.
13. Then learning the upper-layer weight matrix " "'U " "'given other weights in the network can be formulated as a convex optimization problem:
14. A common practice for solving a biconvex problem ( which does not guarantee global optimality of the solution ) is alternatively updating x, y by fixing one of them and solving the corresponding convex optimization problem.
15. His work with Arkadi Nemirovski in the 1994 book is the first to point out that interior point method can solve convex optimization problems, and the first to make a systematic study of semidefinite programming ( SDP ).
16. If the objective function is a ratio of a concave and a convex function ( in the maximization case ) and the constraints are convex, then the problem can be transformed to a convex optimization problem using fractional programming techniques.
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