We consider the problem of minimizing a polynomial on the hypercube [0, 1]n and derive new error bounds for the hierarchy of semidefinite programming approximations to this problem...
It is known that the Karush-Kuhn-Tucker (KKT) conditions of semidefinite programming can be reformulated as a nonsmooth system via the metric projector over the cone of symmetric ...
Recently, a semidefinite programming (SDP) relaxation approach has been proposed to solve the sensor network localization problem. Although it achieves high accuracy in estimating ...
Semidefinite optimization, commonly referred to as semidefinite programming, has been a remarkably active area of research in optimization during the last decade. For combinatoria...
Semidefinite Programming (SDP) may be seen as a generalization of Linear Programming (LP). In particular, one may extend interior point algorithms for LP to SDP, but it has proven...
We investigate the Semidefinite Programming based Sums of squares (SOS) decomposition method, designed for global optimization of polynomials, in the context of the (Maximum) Sati...
High-order repetitive control has previously been introduced to either improve the robustness for period-time uncertainty or reduce the sensitivity for non-periodic inputs of stan...
Goele Pipeleers, Bram Demeulenaere, Joris De Schut...
Discrepancy is a versatile bound in communication complexity which can be used to show lower bounds in the distributional, randomized, quantum, and even unbounded error models of ...