Exact(40)
Gradient minimization methods need the gradient of a functional to be minimized.
Variance minimization methods : define clusters as blocks in the matrix with minimal deviation of their elements.
In 1973, the young mathematician Claude Lemaréchal was surprised by his success with convex minimization methods on problems that were known to be non-convex; for minimizing nonlinear problems, a solution of the dual problem problem need not provide useful information for solving the primal problem, unless the primal problem be convex and satisfy a constraint qualification.
Hence we employed gradient based minimization methods.
Examples are energy minimization methods and molecular dynamics (MD) simulations.
Local deterministic and global probabilistic minimization methods are studied.
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In many variational problems, weak lower semicontinuity is an essential condition for the existence of minimizers, using the minimization method.
A mutual graph cut-based minimization method is developed to minimize the energy pairs.
A mutual graph cut-based minimization method is developed to minimize the energy pairs. .
Any minimization method can be applied to minimize J.
An optimization procedure, based on a least square minimization method, then gives the gain and cutoff frequency minimizing, where is the transfer function of the high-shelving filter.
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