In this paper we investigate the sub-channel assignment and power control to maximize the total sum rate in the uplink of two-cell network. It is assumed that there are some sub-channels in each cell which should be allocated among some users. Also, each user is subjected to a power constraint. The underlying problem is a non-convex mixed integer non-linear optimization problem which does not h...

We study the convex optimization approach for parameter estimation of several sinusoidal models, namely, single complex/real tone, multiple complex sinusoids, and single two-dimensional complex tone, in the presence of additive Gaussian noise. The major difficulty for optimally determining the parameters is that the corresponding maximum likelihood (ML) estimators involve finding the global min...

We present a new approach to estimate the risk-neutral probability density function (pdf) of the future prices of an underlying asset from the prices of options written on the asset. The estimation is carried out in the space of cubic spline functions, yielding appropriate smoothness. The resulting optimization problem, used to invert the data and determine the corresponding density function, i...

In this work we propose a general procedure for estimating the global minima of mathematical programs given in the general form as follows: min P0 (t) s.t. Pi (t) ≤ 0 for i = 1, . . . , k (Π) , where the functions Pi : R n → R are n-dimensional polynomials which are supposed to be non convex. The theory behind the Method of Moments guarantees that all global minima of the non convex program (Π)...

knowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering  in which there is no need to  be peculiar about how to select initial values. due to properly converting the task of optimization to an equivalent...

An innnite-dimensional convex optimization problem with the linear-quadratic cost function and linear-quadratic constraints is considered. We generalize the interior-point techniques of Nesterov-Nemirovsky to this innnite-dimensional situation. The obtained complexity estimates are similar to nite-dimensional ones. We apply our results to the linear-quadratic control problem with quadratic cons...

We present efficiently verifiable sufficient conditions for the validity of specific NP-hard semi-infinite systems of semidefinite and conic quadratic constraints arising in the framework of Robust Convex Programming and demonstrate that these conditions are “tight” up to an absolute constant factor. We discuss applications in Control on the construction of a quadratic Lyapunov function for lin...

We show that SDP (semidefinite programming) and SOCP (second order cone programming) relaxations provide exact optimal solutions for a class of nonconvex quadratic optimization problems. It is a generalization of the results by S. Zhang for a subclass of quadratic maximization problems that have nonnegative off-diagonal coefficient matrices of objective quadratic functions and diagonal coeffici...

We consider the general nonlinear optimization problem in 01 variables and provide an explicit equivalent convex positive semidefinite program in 2 − 1 variables. The optimal values of both problems are identical. From every optimal solution of the former one easily find an optimal solution of the latter and conversely, from every solution of the latter one may construct an optimal solution of ...

Abstract Why is it that semidefinite relaxations have been so successful in numerous applications computer vision and robotics for solving non-convex optimization problems involving rotations? In studying the empirical performance, we note there are few failure cases reported literature, particular estimation with a single rotation, motivating us to gain further theoretical understanding. A gen...