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The paper discusses two methods of optimal excitation signal design for identification with Maximum Likelihood parameter estimation: The ‘classical’, dispersion function based method, and a new, semid...
In semidefinite programming we minimize a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmo...
We point out some connections between applications of semidefinite programming in control and in combinatorial optimization. In both fields semidefinite programs arise as convex relaxations of NP-hard...
Since 1984 there has been a concentrated effort to develop efficient interior-point methods for linear programming (LP). In the last few years researchers have begun to appreciate a very important pro...
Some interesting semi-infinite optimization problems can be reduced to semidefinite optimization problems, and hence solved efficiently using recent interior-point methods. In this paper we discuss se...
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 ...
We present an efficient method for optimal design and synthesis of CMOS inductors for use in RF circuits. This method uses the the physical dimensions of the inductor as the design parameters and hand...
A wide variety of nonlinear convex optimization problems can be cast as problems involving linear matrix inequalities (LMIs), and hence efficiently solved using recently developed interior-point metho...
We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indices for the overall amplifier, such as band...
We describe a new method for determining component values and transistor dimensions for CMOS operational amplifiers (op-amps). We observe that a wide variety of design objectives and constraints have ...
We describe an efficient method for solving an optimal control problem that arises in robust model-predictive control. The problem is to design the input sequence that minimizes the peak tracking erro...
In this talk I will give an overview of some major developments in convex optimization that have emerged over the last ten years or so. The basic idea is that convex problems are fundamentally tractab...
We show that the Lagrange dual problems of the channel capacity problem with input cost and the rate distortion problem are simple geometric programs. Upper bounds on channel capacity and lower bounds...
The general setting we consider involves a process, iteration, or method in which the computation or communication at each step is local, determined by a given graph, and involves some parameters or c...
Geometric programming (GP) describes a type of optimization problem that has been known since the 1970s, but recently has attracted more attention for several reasons. The first is the development of ...

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