Mass minimization and material cost minimization are also considered.
An absolute minimization was also proposed to decrease the probability of being trapped in a local minimum.
The applicability and significance of the identified critical factors for effectively designing waste minimization are also explored.
We have to emphasize that although (l_{p} -minimization is al_{p} -minimizationo iss nonconvexity alsononsmoothness, a lot of algorithms have been difficultto solve (l_{p})-minimization; see e.g. [11, 19], andue20].
There exists a constant (p(A,b >0) such that every solution to (l_{p} -minimization is al_{p} -minimization (l_{0})-misimizalson whenever (0< p< p(A,b)).
Recently, Peng, Yue, and Li [7] have proved that there exists a constant (p(A,b >0) such that every solution of (l_{p} -minimization is al_{p} -minimizationl_{0})-misimizalson whenever (0< p< p(a,b)).
Comparisons between the gradient enhanced approach and the standard ℓ1-minimization are also presented and numerical examples suggest that the inclusion of derivative information can guarantee sparse recovery at a reduced computational cost.
Because including diffuse functions in the basis set is relatively more important for anionic species, potential energy minimizations were also run with the B3LYP/6-31+G d B3LYP/6-31+G dhe H,pC and O atomethod these twonconfigurathens.
Besides energy minimization, it is also aimed to map the cores such that a thermally balanced Network-on-Chip design is obtained.
The dual of the minimization problem is also formulated and is solved in order to present the statics (thrust curve, locus of feasible internal reactions, etc). of the reinforced arch as a consequence of the kinematical assumptions used in the primal minimization problem.
