Optimal Design by Geometric Programming

Beightler, Charles S.; Lo, Ta-Chen; Rylander, H. G.

doi:10.1115/1.3427707

Cited by 31 publications

(26 citation statements)

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“…Following [11], [14], an iterative procedure can be used where at each iteration the standard GP (21)- (23) is solved for an updated set of {γ k }. Assuming a feasible set of magnitude…”

Section: B Amplitude Optimizationmentioning

confidence: 99%

Waveform optimization for Wireless Power Transfer with nonlinear energy harvester modeling

Clerckx

Bayguzina

Yates

et al. 2015

2015 International Symposium on Wireless Communication Systems (ISWCS)

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Abstract-Far-field Wireless Power Transfer (WPT) and Simultaneous Wireless Information and Power Transfer (SWIPT) have attracted significant attention in the RF and communication communities. Despite the rapid progress, the problem of waveform design to enhance the output DC power of wireless energy harvester has received limited attention so far. In this paper, we bridge communication and RF design and derive novel multisine waveforms for multi-antenna wireless power transfer. The waveforms are adaptive to the channel state information and result from a posynomial maximization problem that originates from the non-linearity of the energy harvester. They are shown through realistic simulations to provide significant gains (in terms of harvested DC power) over state-of-the-art waveforms under a fixed transmit power constraint.

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“…Following [11], [14], an iterative procedure can be used where at each iteration the standard GP (21)- (23) is solved for an updated set of {γ k }. Assuming a feasible set of magnitude…”

Section: B Amplitude Optimizationmentioning

confidence: 99%

Waveform optimization for Wireless Power Transfer with nonlinear energy harvester modeling

Clerckx

Bayguzina

Yates

et al. 2015

2015 International Symposium on Wireless Communication Systems (ISWCS)

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“…Geometric programming is one particular form of convex optimization, which traditionally has been used for engineering design [5,1], and more recently also for analog circuit sizing [9].…”

Section: Introductionmentioning

confidence: 99%

Automated design of operational transconductance amplifiers using reversed geometric programming

Vanderhaegen

Brodersen

2004

Proceedings of the 41st Annual Design Automation Conference

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We present a method for designing operational amplifiers using reversed geometric programming, which is an extension of geometric programming that allows both convex and non-convex constraints. Adding a limited set of non-convex constraints can improve the accuracy of convex equationbased optimization, without compromising global optimality. These constraints allow increased accuracy for critical modeling equations, such as the relationship between gm and IDS . To demonstrate the design methodology, a foldedcascode amplifier is designed in a 0.18 µm technology for varying speed requirements and is compared with simulations and designs obtained from geometric programming.

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“…Geometric programming problems with posynomials are known to have a unique, globally optimal solution [18]. The example in this section has a quadratic object, 14 variables, 6 inequality constraints, and 4 equality constraints, along with non negativity constraints on the variables, as shown in Equation 14.…”

Section: A Geometric Programming Problemmentioning

confidence: 99%

Enhanced Collaborative Optimization: A Decomposition-Based Method for Multidisciplinary Design

Roth

Kroo

2008

Volume 1: 34th Design Automation Conference, Parts a and B

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Astute choices made early in the design process provide the best opportunity for reducing the life cycle cost of a new product. Optimal decisions require reasonably detailed disciplinary analyses, which pose coordination challenges. These types of complex multidisciplinary problems are best addressed through the use of decomposition-based methods, several of which have recently been developed. Two of these methods are collaborative optimization (CO) and analytical target cascading (ATC). CO was conceived in 1994 in response to multidisciplinary design needs in the aerospace industry. Recent progress has led to an updated version, enhanced collaborative optimization (ECO), that is introduced in this paper. ECO addresses many of the computational challenges inherent in CO, yielding significant computational savings and more robust solutions. ATC was formalized in 2000 to address needs in the automotive industry. While ATC was originally developed for object-based decomposition, it is also applicable to multidisciplinary design problems. In this paper, both methods are applied to a set of test cases. The goal is to introduce the ECO methodology by comparing and contrasting it with ATC, a method familiar within the mechanical engineering design community. Comparison of ECO and ATC is not intended to establish the computational superiority of either method. Rather, these two methods are compared as a means of highlighting several promising approaches to the coordination of distributed design problems.

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Optimal Design by Geometric Programming

Cited by 31 publications

References 0 publications

Waveform optimization for Wireless Power Transfer with nonlinear energy harvester modeling

Waveform optimization for Wireless Power Transfer with nonlinear energy harvester modeling

Automated design of operational transconductance amplifiers using reversed geometric programming

Enhanced Collaborative Optimization: A Decomposition-Based Method for Multidisciplinary Design

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