National Repository of Grey Literature 20 records found  previous11 - 20  jump to record: Search took 0.00 seconds. 
Metody s proměnnou metrikou s omezenou pamětí, založené na invariantních maticích
Vlček, Jan ; Lukšan, Ladislav
A new class of limited-memory variable metric methods for unconstrained minimization is described. Approximations of inverses of Hessian matrices are based on matrices which are invariant with respect to a linear transformation. As these matrices are singular, they are adjusted for a computation of direction vectors. The methods have the quadratic termination property, which means that they will find a minimum of a strict quadratic function with an exact choice of a step-length after a finite number of steps. Numerical experiments show the efficiency of this method.
New Limited-Memory Variable Metric Methods for Unconstrained Optimization
Vlček, Jan ; Lukšan, Ladislav
A new family of numerically efficient variable metric or quasi-Newton methods for unconstrained optimizaion is adapted for large-scale optimization. Global convergence of the method can be established for convex sufficiently smooth functions. Some encouraging numerical experience is reported. We refer to our report V-876 for proof and details.

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