Results 1  10
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20
Guaranteed minimumrank solutions of linear matrix equations via nuclear norm minimization
, 2007
"... The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative ..."
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Cited by 218 (15 self)
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The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative filtering. Although specific instances can often be solved with specialized algorithms, the general affine rank minimization problem is NPhard, because it contains vector cardinality minimization as a special case. In this paper, we show that if a certain restricted isometry property holds for the linear transformation defining the constraints, the minimum rank solution can be recovered by solving a convex optimization problem, namely the minimization of the nuclear norm over the given affine space. We present several random ensembles of equations where the restricted isometry property holds with overwhelming probability, provided the codimension of the subspace is sufficiently large. The techniques used in our analysis have strong parallels in the compressed sensing framework. We discuss how affine rank minimization generalizes this preexisting concept and outline a dictionary relating concepts from cardinality minimization to those of rank minimization. We also discuss several algorithmic approaches to solving the norm minimization relaxations, and illustrate our results with numerical examples.
Enhancing Sparsity by Reweighted ℓ1 Minimization
, 2007
"... It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many si ..."
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Cited by 76 (5 self)
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It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted ℓ1minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed nearsparsity in overcomplete representations—not by reweighting the ℓ1 norm of the coefficient sequence as is common, but by reweighting the ℓ1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as compressed sensing.
Optimal design of a CMOS opamp via geometric programming
 IEEE Transactions on ComputerAided Design
, 2001
"... We describe a new method for determining component values and transistor dimensions for CMOS operational ampli ers (opamps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result the ampli er ..."
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Cited by 51 (10 self)
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We describe a new method for determining component values and transistor dimensions for CMOS operational ampli ers (opamps). We observe that a wide variety of design objectives and constraints have a special form, i.e., they are posynomial functions of the design variables. As a result the ampli er design problem can be expressed as a special form of optimization problem called geometric programming, for which very e cient global optimization methods have been developed. As a consequence we can e ciently determine globally optimal ampli er designs, or globally optimal tradeo s among competing performance measures such aspower, openloop gain, and bandwidth. Our method therefore yields completely automated synthesis of (globally) optimal CMOS ampli ers, directly from speci cations. In this paper we apply this method to a speci c, widely used operational ampli er architecture, showing in detail how to formulate the design problem as a geometric program. We compute globally optimal tradeo curves relating performance measures such as power dissipation, unitygain bandwidth, and openloop gain. We show how the method can be used to synthesize robust designs, i.e., designs guaranteed to meet the speci cations for a
Enhacing sparsity by reweighted ℓ1 minimization
 Journal of Fourier Analysis and Applications
, 2008
"... It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many si ..."
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Cited by 34 (1 self)
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It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted ℓ1minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed nearsparsity in overcomplete representations—not by reweighting the ℓ1 norm of the coefficient sequence as is common, but by reweighting the ℓ1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as compressed sensing.
Disciplined convex programming
 Global Optimization: From Theory to Implementation, Nonconvex Optimization and Its Application Series
, 2006
"... ..."
Sensor selection via convex optimization
 IEEE Transactions on Signal Processing
, 2009
"... Abstract—We consider the problem of choosing a set of sensor measurements, from a set of possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the possible choices of sensor measurements is no ..."
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Cited by 22 (2 self)
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Abstract—We consider the problem of choosing a set of sensor measurements, from a set of possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the possible choices of sensor measurements is not practical unless and are small. In this paper, we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of operations; for 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2GHz personal computer. Index Terms—Convex optimization, experiment design, sensor selection. I.
Compressed sensing with quantized measurements
, 2010
"... We consider the problem of estimating a sparse signal from a set of quantized, Gaussian noise corrupted measurements, where each measurement corresponds to an interval of values. We give two methods for (approximately) solving this problem, each based on minimizing a differentiable convex function p ..."
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Cited by 17 (0 self)
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We consider the problem of estimating a sparse signal from a set of quantized, Gaussian noise corrupted measurements, where each measurement corresponds to an interval of values. We give two methods for (approximately) solving this problem, each based on minimizing a differentiable convex function plus an regularization term. Using a first order method developed by Hale et al, we demonstrate the performance of the methods through numerical simulation. We find that, using these methods, compressed sensing can be carried out even when the quantization is very coarse, e.g., 1 or 2 bits per measurement.
Relaxed Maximum a Posteriori Fault Identification
, 2007
"... We consider the problem of estimating a pattern of faults, represented as a binary vector, from a set of measurements. The measurements can be noise corrupted real values, or quantized versions of noise corrupted signals, including even 1bit (sign) measurements. Maximum a posteriori probability (MA ..."
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Cited by 12 (9 self)
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We consider the problem of estimating a pattern of faults, represented as a binary vector, from a set of measurements. The measurements can be noise corrupted real values, or quantized versions of noise corrupted signals, including even 1bit (sign) measurements. Maximum a posteriori probability (MAP) estimation of the fault pattern leads to a difficult combinatorial optimization problem, so we propose a variation in which an approximate maximum a posteriori probability estimate is found instead, by solving a convex relaxation of the original problem, followed by rounding and simple local optimization. Our method is extremely efficient, and scales to very large problems, involving thousands (or more) possible faults and measurements. Using synthetic examples, we show that the method performs extremely well, both in identifying the true fault pattern, and in identifying an ambiguity group, i.e., a set of alternate fault patterns that explain the observed measurements almost as well as our estimate. 1
An Efficient Technique for Device and Interconnect Optimization in Deep Submicron Designs
 in Proc. Int. Symp. on Physical Design
, 1997
"... In this paper, we formulated a new class of optimization problem, named the general CHposynomial program, which is more general than the simple and boundedvariation CHposynomial programs in [1]. We revealed the general dominance property so that an efficient and unified algorithm based on the loc ..."
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Cited by 7 (2 self)
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In this paper, we formulated a new class of optimization problem, named the general CHposynomial program, which is more general than the simple and boundedvariation CHposynomial programs in [1]. We revealed the general dominance property so that an efficient and unified algorithm based on the local refinement (LR) operation can be used to optimize the simple, boundedvariation and general CHposynomial programs. We applied the LRbased optimization algorithm to solve the device sizing problem using accurate tablebased model, and the wire sizing and spacing problem with consideration of coupling between multiple nets. Both problems are solved in the context of simultaneous device and wire sizing optimization for deep submicron designs. Experiments show that our LRbased optimization algorithm is very effective and extremely efficient. Up to 16.5% delay reduction is observed when compared with previous work based on the simple device model [1], and up to 31% delay reduction and 100x speedup is observed when compared the global interconnect sizing and spacing work [2]. We believe that our general CHposynomial formulation and LRbased algorithm can also be applied to other optimization problems in the CAD field.
Theory and Algorithm of LocalRefinement Based Optimization with Application to Device and Interconnect Sizing
, 1999
"... In this paper we formulate three classes of optimization problems: the simple, monotonicallyconstrained, and bounded CHprograms. We reveal the dominance property under the local refinement (LR) operation for the simple CHprogram, as well as the general dominance property under the pseudoLR opera ..."
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Cited by 7 (7 self)
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In this paper we formulate three classes of optimization problems: the simple, monotonicallyconstrained, and bounded CHprograms. We reveal the dominance property under the local refinement (LR) operation for the simple CHprogram, as well as the general dominance property under the pseudoLR operation for the monotonicallyconstrained CHprogram and the extendedLR operation for the bounded CHprogram. These properties enable a very efficient polynomialtime algorithm, using different types of LR operations to compute tight lower and upper bounds of the exact solution to any CHprogram. We show that the algorithm is capable of solving many layout optimization problems in deep submicron IC and/or highperformance MCM/PCB designs. In particular, we apply...