Matches in SemOpenAlex for { <https://semopenalex.org/work/W2551293070> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W2551293070 abstract "Calculating or approximating the derivatives for large-scale multi-dimensional functions is an active research area in modern mathematics. The rationale behind this popularity is its wide applications in statistics, financial mathematics and portfolio optimization. For example, in statistics, maximum likelihood estimation seeks the value of parameter vector that maximize the likelihood function, which requires to find the points where derivative of likelihood function is zero; in finance, taking derivatives can be applied to sensitivity analysis for equity valuation, with respect to single or multiple variables; numerical optimization problems calculate the derivatives at each iteration, such as trust-region method. The most straightforward way to calculate a derivative is hand-coding, but it is only applicable to sufficiently simple functions, and it’s error-prone. Finite-difference method is easy to implement with a programming language, but the accuracy depends on the choice of discretization steps, and hence can not be guaranteed. Throughout this essay, we study the methodology of automatic differentiation (AD), and introduce a structural automatic differentiation method (Structural-AD) for calculating gradient and hessian. The implementation of Structural-AD uses the software ADMAT 2.0 installed on MATLAB by Cayuga Research Associates [2009]. Structura-AD exploits the ‘natural structure’ of some functions and it makes use of the regular reverse mode of AD to achieve more efficient computing time and less memory usage requirement. In the paper by Xu and Coleman [2013], they applied structural-AD to two extreme cases, generalized partially separable problem and dynamic system computations. They showed that computing time and memory requirement using Structural-AD is significantly reduced compared to the regular reverse mode. We also applied structural-AD on a Conditional Value-at-Risk (CVaR) optimization problem, specifically with the underlying function describing the loss function of a stock portfolio." @default.
- W2551293070 created "2016-11-30" @default.
- W2551293070 creator A5050311492 @default.
- W2551293070 date "2013-01-01" @default.
- W2551293070 modified "2023-09-24" @default.
- W2551293070 title "A Structural and Ecient Method for Calculating Gradient and Hessian with CVaR Portfolio Optimization Application" @default.
- W2551293070 cites W1996360328 @default.
- W2551293070 cites W2044733003 @default.
- W2551293070 cites W624564079 @default.
- W2551293070 hasPublicationYear "2013" @default.
- W2551293070 type Work @default.
- W2551293070 sameAs 2551293070 @default.
- W2551293070 citedByCount "0" @default.
- W2551293070 crossrefType "journal-article" @default.
- W2551293070 hasAuthorship W2551293070A5050311492 @default.
- W2551293070 hasConcept C10138342 @default.
- W2551293070 hasConcept C105795698 @default.
- W2551293070 hasConcept C11413529 @default.
- W2551293070 hasConcept C126255220 @default.
- W2551293070 hasConcept C133512626 @default.
- W2551293070 hasConcept C137836250 @default.
- W2551293070 hasConcept C146880194 @default.
- W2551293070 hasConcept C162324750 @default.
- W2551293070 hasConcept C202655437 @default.
- W2551293070 hasConcept C203616005 @default.
- W2551293070 hasConcept C2779922397 @default.
- W2551293070 hasConcept C2780821815 @default.
- W2551293070 hasConcept C28826006 @default.
- W2551293070 hasConcept C33923547 @default.
- W2551293070 hasConcept C41008148 @default.
- W2551293070 hasConcept C45374587 @default.
- W2551293070 hasConcept C5496284 @default.
- W2551293070 hasConceptScore W2551293070C10138342 @default.
- W2551293070 hasConceptScore W2551293070C105795698 @default.
- W2551293070 hasConceptScore W2551293070C11413529 @default.
- W2551293070 hasConceptScore W2551293070C126255220 @default.
- W2551293070 hasConceptScore W2551293070C133512626 @default.
- W2551293070 hasConceptScore W2551293070C137836250 @default.
- W2551293070 hasConceptScore W2551293070C146880194 @default.
- W2551293070 hasConceptScore W2551293070C162324750 @default.
- W2551293070 hasConceptScore W2551293070C202655437 @default.
- W2551293070 hasConceptScore W2551293070C203616005 @default.
- W2551293070 hasConceptScore W2551293070C2779922397 @default.
- W2551293070 hasConceptScore W2551293070C2780821815 @default.
- W2551293070 hasConceptScore W2551293070C28826006 @default.
- W2551293070 hasConceptScore W2551293070C33923547 @default.
- W2551293070 hasConceptScore W2551293070C41008148 @default.
- W2551293070 hasConceptScore W2551293070C45374587 @default.
- W2551293070 hasConceptScore W2551293070C5496284 @default.
- W2551293070 hasLocation W25512930701 @default.
- W2551293070 hasOpenAccess W2551293070 @default.
- W2551293070 hasPrimaryLocation W25512930701 @default.
- W2551293070 hasRelatedWork W135104305 @default.
- W2551293070 hasRelatedWork W1502893368 @default.
- W2551293070 hasRelatedWork W194653768 @default.
- W2551293070 hasRelatedWork W1978359677 @default.
- W2551293070 hasRelatedWork W2011892549 @default.
- W2551293070 hasRelatedWork W2042023521 @default.
- W2551293070 hasRelatedWork W2074976803 @default.
- W2551293070 hasRelatedWork W209789590 @default.
- W2551293070 hasRelatedWork W2173316741 @default.
- W2551293070 hasRelatedWork W2258463707 @default.
- W2551293070 hasRelatedWork W2290899512 @default.
- W2551293070 hasRelatedWork W2410480129 @default.
- W2551293070 hasRelatedWork W2787407524 @default.
- W2551293070 hasRelatedWork W2807044327 @default.
- W2551293070 hasRelatedWork W2904881196 @default.
- W2551293070 hasRelatedWork W2906263529 @default.
- W2551293070 hasRelatedWork W3007638363 @default.
- W2551293070 hasRelatedWork W3037674050 @default.
- W2551293070 hasRelatedWork W3083961288 @default.
- W2551293070 hasRelatedWork W2184333832 @default.
- W2551293070 isParatext "false" @default.
- W2551293070 isRetracted "false" @default.
- W2551293070 magId "2551293070" @default.
- W2551293070 workType "article" @default.