Sentence examples for norm formulation from inspiring English sources

Our mixed norm formulation is mathematically equivalent to conventional L2 constrained L1 minimization, but fundamentally different in the non-convex topology we employ to solve for and track the optimal coefficients in real time directly over the complex field.

However, it will be seen that MCM denoising does not lend itself readily to a dual norm formulation, and thus cannot avail of the better quality results obtained from such a formulation.

The approach is based on ∞-norm formulation and can accommodate both preemptive and non-preemptive goal programming.

For limiting the stress, a multi-p-norm formulation is proposed to deal with the local nature of stress and to avoid stress concentration.

The proposed norm-oriented formulation extends the goal-oriented formulation since it is equation-based and uses an adjoint.

At the same time, the norm-oriented formulation somewhat supersedes the goal-oriented one since it is basically a solution-convergent method.

The sparsity constraints due to the ℓ1 norm in the formulation allow us to select a subset of the covariates (i.e. the sequence features) to regress upon.

Early statements about moral-value judgments, such as those by Carnap or by A.J. Ayer, a more radical British positivist, seemed shocking to many philosophers, to whom it seemed that, in their careless formulation, moral norms were to be treated like expressions of taste.

A novel source-to-sensor association scheme and tracking is introduced by enhancing the standard Kalman filtering minimization formulation with norm-one regularization terms.

We commence by reviewing some preliminaries of fractional order functional spaces endowed with inner products and norms, and the weak formulation for the space-fractional reaction advection diffusion equation is given in the next section.

The use of the zero norm leads to a nonconvex formulation, which is solved by using Difference of Convex (DC) functions, extending DC programming to second-order cones.