0410721 20240103182234.420060210235959.9 Basel Birkhäuser 2001 13 s. 242-254HoffmannK. H.HoppeR. H. W.SchultzV. Young measures DiPerna-Majda measures approximation cav_un_auth*0101187 Roubíček Tomáš UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101142 Kružík Martin UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. 12A IAA1075005 GA AV ČR cav_un_auth*0012782 GA201/00/0768 GA ČR cav_un_auth*0005685 AV0Z1075907 Nonconvex optimization problems need a relaxation to handle effectively fast oscillation (and possibly also concentration) effects. This uses Young measures or their generalizations. Approximation of the relaxed problem can then be made by various ways, but computationally the most effective way appears to use adaptively a maximum principle (if it forms also a sufficient optimality condition) with the Hamiltonian guessed approximately from a previous iteration, e.g. from a coarser mesh. cav_un_auth*0212855 Fast Solution of Discretized Optimization Problems Berlin DE 12.06.2000-14.06.2000 BA MTR http://hdl.handle.net/11104/0130809 UTIA-B 20010190 2001 2001 Basel Birkhäuser Proceedings of the Conference Fast Solution of Discretized Optimization Problems 242 254 Hoffmann K. H. 340 Hoppe R. H. W. 340 Schultz V. 340