Lieven Vandenberghe

Semidefinite programming

In semidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefinite programming unifies several standard problems (e.g., linear and quadratic programming) and finds many applications in engineering and combinatorial optimization. Although semidefinite programs are much more general than linear programs, they are not much harder to solve. Most interior-point methods for linear programming have been generalized to semidefinite programs. As in linear programming, these methods have polynomial worst-case complexity and perform very well in practice. This paper gives a survey of the theory and applications of semidefinite programs and an introduction to primaldual interior-point methods for their solution.

Applications of second-Order cone programming '

In a second-Order cone program (SOCP) a linear function is minimized over the intersection of an affine set and the product of second-Order (quadratic) cones. SOCPs are nonlinear convex Problems that include linear and (convex) quadratic programs as special cases, but are less general than semidefinite programs (SDPs). Several efficient primaldual interior-Point methods for SOCP have been developed in the last few years. After reviewing the basic theory of SOCPs, we describe general families of Problems that tan be recast as SOCPs. These include robust linear programming and robust leastsquares Problems, Problems involving sums or maxima of norms, or with convex hyperbolic constraints. We discuss a variety of engineering applications, such as filter design, antenna array weight design, truss design, and grasping forte optimization in robotics. We describe an efficient primaldual interior-Point method for solving SOCPs, which shares many of the features of primaldual interior-Point methods for linear program

Determinant Maximization with Linear Matrix Inequality Constraints

The problem of maximizing the determinant of a matrix subject to linear matrix inequalities (LMIs) arises in many fields, including computational geometry, statistics, system identification, experiment design, and information and communication theory. It can also be considered as a generalization of the semidefinite programming problem. We give an overview of the applications of the determinant maximization problem, pointing out simple cases where specialized algorithms or analytical solutions are known. We then describe an interior-point method, with a simplified analysis of the worst-case complexity and numerical results that indicate that the method is very efficient, both in theory and in practice. Compared to existing specialized algorithms (where they are available), the interior-point method will generally be slower; the advantage is that it handles a much wider variety of problems.

On- and off-line identification of linear state-space models

Abstract A geometrically inspired matrix algorithm is derived for the identification of statespace models for multivariable linear time-invariant systems using (possibly noisy) input-output (I/O) measurements only. As opposed to other (mostly stochastic) identification schemes, no variance-covariance information whatever is involved, and only a limited number of I/O-data are required for the determination of the system matrices. Hence, the algorithm can be best described and understood in the matrix formalism, and consists of the following two steps. First, a state vector sequence is realized as the intersection of the row spaces of two block Hankel matrices, constructed with I/O-data. Then the system matrices are obtained at once from the least-squares solution of a set of linear equations. When dealing with noisy data, this algorithm draws its excellent performance from repeated use of the numerically stable and accurate singular value decomposition. Also, the algorithm is easily applied to slowly time-...

Interior-Point Method for Nuclear Norm Approximation with Application to System Identification

The nuclear norm (sum of singular values) of a matrix is often used in convex heuristics for rank minimization problems in control, signal processing, and statistics. Such heuristics can be viewed as extensions of

FIR filter design via semidefinite programming and spectral factorization

\ell_1

The generalized linear complementarity problem applied to the complete analysis of resistive piecewise-linear circuits

-norm minimization techniques for cardinality minimization and sparse signal estimation. In this paper we consider the problem of minimizing the nuclear norm of an affine matrix-valued function. This problem can be formulated as a semidefinite program, but the reformulation requires large auxiliary matrix variables, and is expensive to solve by general-purpose interior-point solvers. We show that problem structure in the semidefinite programming formulation can be exploited to develop more efficient implementations of interior-point methods. In the fast implementation, the cost per iteration is reduced to a quartic function of the problem dimensions and is comparable to the cost of solving the approximation problem in the Frobenius norm. In the second part of the paper, the nuclear norm approximation algorithm is applied to system identification. A variant of a simple subspace algorithm is presented in which low-rank matrix approximations are computed via nuclear norm minimization instead of the singular value decomposition. This has the important advantage of preserving linear matrix structure in the low-rank approximation. The method is shown to perform well on publicly available benchmark data.

FIR Filter Design via Spectral Factorization and Convex Optimization

We present a semidefinite programming approach to FIR filter design with arbitrary upper and lower bounds on the frequency response magnitude. It is shown that the constraints can be expressed as linear matrix inequalities (LMIs), and hence they can be easily handled by interior-point methods. Using this LMI formulation, we can cast several interesting filter design problems as convex or quasi-convex optimization problems, e.g. minimizing the length of the FIR filter and computing the Chebychev approximation of a desired power spectrum or a desired frequency response magnitude on a logarithmic scale.

Extended LMI characterizations for stability and performance of linear systems

An important application of complementarity theory consists in solving sets of piecewise-linear equations and hence in the analysis of piecewise-linear resistive circuits. The authors show how a generalized version of the linear complementarity problem can be used to analyze a broad class of piecewise-linear circuits. Nonlinear resistors that are neither voltage nor current controlled can be allowed, and no restrictions on the linear part of the circuit have to be made. As a second contribution, the authors describe an algorithm for the solution of the generalized complementarity problem and show how it can be applied to yield a complete description of the DC solution set as well as of driving-point and transfer characteristics. >

Semidefinite programming duality and linear time-invariant systems

We consider the design of finite impulse response (FIR) filters subject to upper and lower bounds on the frequency response magnitude. The associated optimization problems with the filter coefficients as the variables and the frequency response bounds as constraints are in general nonconvex. Using a change of variables and spectral factorization we can pose such problems as linear or nonlinear convex optimization problems. As a result we can solve them efficiently (and globally) by recently developed interior-point methods. We describe applications to filter and equalizer design and the related problem of antenna array weight design.