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We prove an effective commutativity criterion and classify Gelfand pairs under two mild technical constraints.
      
Orthogonality conditions for ?1, …, ?q naturally impose constraints on the scaling coefficients, which are then called the wavelet matrix.
      
Sequential quadratic programming methods for optimal control problems with state constraints
      
A kind of direct methods is presented for the solution of optimal control problems with state constraints.
      
At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function.
      
With almost the same but somewhat more relaxed constraints on the multiple splittings, we prove the convergence and estimate the convergence rate of the new method.
      
In this paper, we provide a new generalized gradient projection algorithm for nonlinear programming problems with linear constraints.
      
In this paper, a new superlinearly convergent algorithm is presented for optimization problems with general nonlinear equality and inequality constraints.
      
Second, the sequential points generated by the algorithm satisfy all inequality constraints and its step-length is computed by the straight line search.
      
In this paper, a quasidifferentiable programming problem with inequality constraints is considered.
      
This paper presents a trust region algorithm for nonlinear optimization with linear inequality constraints.
      
In this paper, the nonlinear complementarity problem is transformed into the least squares problem with nonnegative constraints, and a SQP algorithm for this reformulation based on a damped Gauss-Newton type method is presented.
      
This paper is concerned with the decentralized stabilization of continuous and discrete linear interconnected systems with the structural constraints, about the interconnection matrices.
      
A potential reduction algorithm is proposed for optimization of a convex funnction subject to linear constraints.
      
Under suitable assumptions, the global convergence of this algorithm is proved without assuming the linear independence of the gradient of active constraints.
      
In this paper, the quadratic program problem and minimum discrimination information (MDI) problem with a set of quadratic inequality constraints and entropy constraints of density are considered.
      
The design problem of biorthogonal linear-phase scaling filters and wavelet filters as a quadratic programming problem with the linear constraints is formulated.
      
In this paper, two-stage stochastic quadratic programming problems with equality constraints are considered.
      
Moreover, this method can be easily extended to solve stochastic quadratic programming problems with inequality constraints.
      
The trust region subproblem of our method is to minimize a conic function subject to the linearized constraints and trust region bound.
      
 

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