Renata Sotirov

A Near-Maximum-Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming

In multiple-input multiple-output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP-hard. In this paper, a quasi-ML algorithm based on semi-definite programming (SDP) is proposed. We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models. The proposed relaxation models are also used for soft output decoding in MIMO systems.

Erratum: On Semidefinite Programming Relaxations of the Traveling Salesman Problem

Provided are compositions which include 1-methyl-2-nitro-3-[(3-tetrahydrofuryl)methyl]guanidine and at least one compound of formula (I):wherein, R1 represents a halogen atom or a methyl group, R2 represents a halogen atom or a methyl group and R3 represents a hydrogen atom or a cyano group, as well as a method of controlling cockroaches.

Clique-based facets for the precedence constrained knapsack problem

We consider a knapsack problem with precedence constraints imposed on pairs of items, known as the precedence constrained knapsack problem (PCKP). This problem has applications in manufacturing and mining, and also appears as a subproblem in decomposition techniques for network design and related problems. We present a new approach for determining facets of the PCKP polyhedron based on clique inequalities. A comparison with existing techniques, that lift knapsack cover inequalities for the PCKP, is also presented. It is shown that the clique-based approach generates facets that cannot be found through the existing cover-based approaches, and that the addition of clique-based inequalities for the PCKP can be computationally beneficial, for both PCKP instances arising in real applications, and applications in which PCKP appears as an embedded structure.

On semidefinite programming relaxations of maximum k-section

We derive a new semidefinite programming bound for the maximum

SDP Relaxations for Some Combinatorial Optimization Problems

k

A new library of structured semidefinite programming instances

-section problem. For

Semidefinite programming and eigenvalue bounds for the graph partition problem

k=2