In this paper, we propose an effective full array and sparse adaptive beamforming scheme that can be applied for multiple desired signals based on the branch-and-bound algorithm. Adaptive is realized by improved Capon method. At same time, sidelobe constraint added to reduce level. To pointing errors of signals, response phase signal firstly optimized using auxilary variables while keeping ampl...

We use convex relaxation techniques to produce lower bounds on the optimal value of subset selection problems and generate good approximate solutions. We then explicitly bound the quality of these relaxations by studying the approximation ratio of sparse eigenvalue relaxations. Our results are used to improve the performance of branch-and-bound algorithms to produce exact solutions to subset se...

In this paper we compare the performance of two codes for convex 0-1 mixed integer nonlinear programs on a number of test problems. The rst code uses a branch and bound algorithm. The second code is a commercially available implementation of an outer approximation algorithm. The comparison demonstrates that both approaches are generally capable of solving the test problems. However, there are s...

The minimum k-enclosing ball problem seeks the ball with smallest radius that contains at least k of m given points in a general n-dimensional Euclidean space. This problem is NP-hard. We present a branch-and-bound algorithm on the tree of the subsets of k points to solve this problem. The nodes on the tree are ordered in a suitable way, which, complemented with a last-in-first-out search strat...

For linear systems that contain unspeciied parameters that lie in given intervals, we present a branch and bound algorithm for computing the maximum H 1-norm over the set of uncertain parameters.

We present an online learning approach to variable branching in branch-and-bound for mixed-integer linear problems. Our approach consists in learning strong branching scores in an online fashion and in using them to take branching decisions. More specifically, numerical scores are used to rank the branching candidates. If, for a given variable, the learned approximation is deemed reliable, then...

In this paper we present a classification of parallel branch and bound algorithms and investigate the anomalies which can occur during the execution of such algorithms. We develop sufficient conditions to prevent deceleration anomalies from degrading the performance. Such conditions were already known for some synchronous cases. It turns out that these conditions can be generalized to arbitrary...