Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was proposed as a novel optimization appro...

We consider the problem of cooperative data exchange in a group of wireless clients. In this problem each client initially holds a subset of packets and needs to obtain all packets held by other clients. Each client can broadcast its own packets or a combinations thereof to other clients via an error-free broadcast channel. Assuming that clients know which packets are available to other clients...

We construct a modi ed perceptron algorithm that is deterministic, polynomial and also as fast as previous known algorithms. The algorithm runs in time O(mn log n log(1/ρ)), where m is the number of examples, n the number of dimensions and ρ is approximately the size of the margin. We also construct a non-deterministic modi ed perceptron algorithm running in time O(mn log n log(1/ρ)). 1 A Deter...

The problem of maximizing a non-negative submodular function was introduced by Feige, Mirrokni, and Vondrak [FOCS’07] who provided a deterministic local-search based algorithm that guarantees an approximation ratio of 1 3 , as well as a randomized 2 5 -approximation algorithm. An extensive line of research followed and various algorithms with improving approximation ratios were developed, all o...

The security of many cryptography techniques depends upon the intractability of the integer-factoring problem. However, in the recent years there has been a great deal of progress in the art of factoring, relaying mostly on nondeterministic methods. This research proposes a new deterministic factoring algorithm, that factors RSA n = p * q, the algorithm running time relays on the number of digi...