000169726 001__ 169726
000169726 005__ 20180913060836.0
000169726 037__ $$aCONF
000169726 245__ $$aTowards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method
000169726 269__ $$a2011
000169726 260__ $$c2011
000169726 336__ $$aConference Papers
000169726 520__ $$aSolving a convex optimization problem within an a priori certified period of time is a challenging problem. This paper discusses the certification of Nesterov’s fast gradient method for problems with a strictly quadratic objective and a feasible set given as the intersection of a parametrized affine set and a convex set constraint. We derive a lower iteration bound for the solution of the dual problem that is obtained from a partial Lagrange Relaxation and propose a new constant step- size rule that we prove to be optimal under mild assumptions. Finally, the certification procedure is applied to a constrained MPC problem and it is shown that the new step-size rule improves convergence significantly.
000169726 700__ $$aRichter, Stefan
000169726 700__ $$aMorari, Manfred
000169726 700__ $$0246471$$g207237$$aJones, Colin
000169726 7112_ $$dDecember, 2011$$cOrlando, Florida$$aIEEE Conference on Decision & Control
000169726 773__ $$tProceedings of the IEEE Conference on Decision & Control
000169726 909C0 $$0252053$$pLA
000169726 909CO $$ooai:infoscience.tind.io:169726$$pconf$$pSTI
000169726 917Z8 $$x207237
000169726 937__ $$aEPFL-CONF-169726
000169726 973__ $$rNON-REVIEWED$$sACCEPTED$$aEPFL
000169726 980__ $$aCONF