Basics of broadband impedance spectroscopy measurements using periodic excitations
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To be published, Cambridge University Introduction to binary signals used in system identification, Handbook of Semidefinite Programming. Kluwer Academic Publishers, Vandenberghe, Entropic proximal operators for nonnegative trigonometric polynomials. Submitted for publication, Vandenberghe, On the equivalence of the primal-dual hybrid gradient method and Douglas-Rachford splitting.
Vandenberghe, Semidefinite representations of gauge functions for structured low-rank matrix decomposition. Optimization 27, Vandenberghe, Total variation image deblurring with space-varying kernel. Computational Optimization and Applications 67, Vandenberghe, Inexact proximal Newton methods for self-concordant functions. Mathematical Methods of Operations Research 85, Vandenberghe, Extensions of semidefinite programming methods for atomic decomposition.
Vandenberghe, Decomposition methods for sparse matrix nearness problems. Matrix Analysis and Applications 36,[pdf]. Andersen, Chordal graphs and semidefinite optimization. Foundations and Trends in Optimization 1,[pdf] [link to final version]. Imaging Sciences 7,[pdf]. Optimization 24,[pdf] [arXiv: Vandenberghe, Sampling method for semidefinite programs with nonnegative Popov function constraints, International Journal of Control 87,[pdf]. Vandenberghe, Nuclear norm system identification with missing inputs and outputs, System and Control Letters 62,[pdf] [Related software].
Vandenberghe, Logarithmic barriers for sparse matrix cones, Optimization Methods and Software 28,[arXiv: Vandenberghe, Subspace system identification via weighted nuclear norm optimization, Proc. CDC, [arXiv: Vandenberghe, Convex optimization techniques in system identification, Proc. Vandenberghe, Interior-point methods for large-scale cone programming.
Vandenberghe, Topology selection in graphical models of autoregressive processes, Journal of Machine Learning Research11,[pdf]. Dahl, Linear matrix inequalities with chordal sparsity patterns and applications to robust quadratic optimization, in Proc. Vandenberghe, Support vector machine training using matrix completion techniques. Unpublished report [pdf] [Related software]. Vandenberghe, Implementation of nonsymmetric interior-point methods for linear optimization over sparse matrix cones, Mathematical Programming Computation[Journal link] [Related software].
Convex relaxations for mixed integer predictive control, Automatica 46, Vandenberghe, Graphical models of autoregressive processes.
Vandenberghe, Semidefinite programming methods for system realization and identification. CDC, [pdf]. Vandenberghe, Interior-point method for nuclear norm approximation with application to system identification, SIAM Journal on Matrix Analysis and Applications31 3, [pdf] [Related software]. Vandenberghe, Optimal splines for rigid motion systems: Vandenberghe, Maximum-likelihood estimation of autoregressive models with conditional independence constraints, Proc.
Roychowdhury, Covariance selection for non-chordal graphs via chordal embedding, Optimization Methods and Software 23 4, [pdf]. Vandenberghe, Robust gate sizing via mean excess delay minimization, Proc. ISPD, [pdf]. Neural Networks 19, Vandenberghe, Model calibration for optical lithography via semidefinite programming, Optimization and Engineering 9,[pdf].
CDC, Hassibi, A tutorial on geometric programming, Optimization and Engineering 8,[pdf]. VLSI Systems 15, Vandenberghe, Interior-point algorithms for sum-of-squares optimization of multidimensional trigonometric polynomials, Proc.
Vandenberghe, Discrete transforms, semidefinite programming, and sum-of-squares representations of nonnegative polynomials, SIAM J. Boyd, Semidefinite programming and multivariate Chebyshev bounds, Proc. Vandenberghe, Maximum likelihood estimation of Gaussian graphical models: Numerical implementation and topology selection. Technical report originally submitted to Journal of Machine Learning Research[pdf].
Yao, Semidefinite programming bounds on the probability of errors of binary communication systems with inexactly known intersymbol interference, IEEE Trans. Theory 51, Yao, Distributed Gauss-Newton method for node localization in wireless sensor networks, Proc. Yang, Techniques for improving the accuracy of geometric programming based analog circuit design optimization, Proc. ICCAD, Hansson, On the implementation of primal-dual interior-point methods for semidefinite programming problems derived from the KYP lemma, Proc.
Control 48,[pdf]. A longer version is available as a technical report. Vandenberghe, A sequential analytic centering approach to the support vector machine, Proc. CDC. Fleury, Approximate maximum-likelihood estimation using semidefinite programming, Proc.
Vandenberghe, Convex optimization problems involving finite autocorrelation sequences, Mathematical Programming, Series A 93,[pdf]. Nouralishahi, Robust linear programming and optimal control, Proc. A longer version originally submitted to Automatica is available as a technical report. Fleury, Robust least-squares estimators based on semidefinite programming, Proc.
Signals, Systems and Computers, Yun, Design of robust global power and ground networks, Proc. ISPD[pdf]. Vandenberghe, Interior-point methods for magnitude filter design, Proc.
Wu, Semidefinite introduction to binary signals used in system identification and determinant maximization. Wesel, Capacity of the binomial channel, or minimax redundancy for memoryless sources, Proc. Vandenberghe, A primal-dual potential reduction method for integral quadratic constraints, Proc. Vandenberghe, Handling nonnegative constraints in spectral estimation, Proc.
Signals, Systems and Computers, [pdf]. Vandenberghe, Efficient solution of linear matrix inequalities for integral quadratic constraints, Proc. Vandenberghe, Applications of semidefinite programming in process control, Proc. Balakrishnan, Semidefinite programming duality and linear system theory: CDC,[pdf]. Boyd, Applications of semidefinite programming, Applied Numerical Mathematics 29,[pdf]. Connections and implications for computation, Proc.
Lebret, Applications of second-order cone programming, Linear Algebra and its Applications, [pdf]. Matrix Analysis and Applications 19,[pdf]. Boyd, Connections between semi-infinite and semidefinite programming. Vandenberghe, FIR introduction to binary signals used in system identification design via spectral factorization and convex optimization, In: Vandenberghe, Linear matrix inequalities for signal processing.
El Gamal, Optimal wire and transistor sizing for circuits with non-tree topology, Proc. ICCAD, [pdf]. Introduction to binary signals used in system identification, Semidefinite programming relaxations of non-convex problems in control and combinatorial optimization.
Vandenberghe, An application of semidefinite programming duality to derive bounds on the norm of a transfer matrix, Proc. Vandenberghe, FIR filter design via semidefinite programming and spectral factorization, Proc. Balakrishnan, Introduction to binary signals used in system identification and software tools for LMI problems in control: Boyd, A primal-dual potential reduction method for problems involving matrix inequalities, Mathematical Programming, Series B, [pdf].
Vandenberghe, Connections between duality in control theory and convex optimization, Proc. ACC, [pdf]. Grant, Efficient convex optimization for engineering design, Proc.
Boyd, Positive definite programming.