標(biāo)題: Titlebook: Algorithmic Differentiation in Finance Explained; Marc Henrard Book 2017 The Editor(s) (if applicable) and The Author(s) 2017 Algorithmic [打印本頁] 作者: Retina 時(shí)間: 2025-3-21 16:08
書目名稱Algorithmic Differentiation in Finance Explained影響因子(影響力)
書目名稱Algorithmic Differentiation in Finance Explained影響因子(影響力)學(xué)科排名
書目名稱Algorithmic Differentiation in Finance Explained網(wǎng)絡(luò)公開度
書目名稱Algorithmic Differentiation in Finance Explained網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Algorithmic Differentiation in Finance Explained被引頻次
書目名稱Algorithmic Differentiation in Finance Explained被引頻次學(xué)科排名
書目名稱Algorithmic Differentiation in Finance Explained年度引用
書目名稱Algorithmic Differentiation in Finance Explained年度引用學(xué)科排名
書目名稱Algorithmic Differentiation in Finance Explained讀者反饋
書目名稱Algorithmic Differentiation in Finance Explained讀者反饋學(xué)科排名
作者: 商談 時(shí)間: 2025-3-21 21:10 作者: 不開心 時(shí)間: 2025-3-22 03:49
https://doi.org/10.1007/978-3-030-87839-9riting code that performs the Algorithmic Differentiation automatically. The developments required are relatively heavy at the start, but from there on, there should be only a minimal development cost.作者: 含糊 時(shí)間: 2025-3-22 08:37
Automatic Algorithmic Differentiation,riting code that performs the Algorithmic Differentiation automatically. The developments required are relatively heavy at the start, but from there on, there should be only a minimal development cost.作者: fatuity 時(shí)間: 2025-3-22 09:25 作者: coagulation 時(shí)間: 2025-3-22 13:05
https://doi.org/10.1007/978-3-030-87839-9riting code that performs the Algorithmic Differentiation automatically. The developments required are relatively heavy at the start, but from there on, there should be only a minimal development cost.作者: cardiopulmonary 時(shí)間: 2025-3-22 21:06 作者: affect 時(shí)間: 2025-3-22 23:29 作者: Crohns-disease 時(shí)間: 2025-3-23 03:28 作者: 獨(dú)特性 時(shí)間: 2025-3-23 06:50
Model Deployment and Challenges,The starting point of everything is obviously the definition of ., the notion we are planning to compute through Algorithmic Differentiation (AD).作者: hemophilia 時(shí)間: 2025-3-23 12:50
Gang Wang,Arridhana Ciptadi,Ali AhmadzadehIt seems there is no easy starting point for reviewing Algorithmic Differentiation in finance. Each function, especially in the simpler one, seems to be an exception with a particular trick more than a general direct application of the methodology.作者: 摘要 時(shí)間: 2025-3-23 16:18
https://doi.org/10.1007/978-3-030-87839-9The title of this chapter may appear a little bit cryptic. The main advertised goal of Algorithmic Differentiation (AD) is to compute in an efficient way the derivatives of functions with respect to their inputs. Each part of this chapter’s title may appear in contraction with that general goal.作者: 無目標(biāo) 時(shí)間: 2025-3-23 21:20
The Principles of Algorithmic Differentiation,The starting point of everything is obviously the definition of ., the notion we are planning to compute through Algorithmic Differentiation (AD).作者: 闡明 時(shí)間: 2025-3-23 22:11
Application to Finance,It seems there is no easy starting point for reviewing Algorithmic Differentiation in finance. Each function, especially in the simpler one, seems to be an exception with a particular trick more than a general direct application of the methodology.作者: meditation 時(shí)間: 2025-3-24 04:40 作者: NAG 時(shí)間: 2025-3-24 08:09 作者: ATRIA 時(shí)間: 2025-3-24 11:02
Automatic Algorithmic Differentiation,riting code that performs the Algorithmic Differentiation automatically. The developments required are relatively heavy at the start, but from there on, there should be only a minimal development cost.作者: FLUSH 時(shí)間: 2025-3-24 18:25 作者: Comprise 時(shí)間: 2025-3-24 22:03
sitivities of a portfolio with machine precision.. .Written by a leading practitioner who works and programmes AD, it offers a practical analysis of all the major applications of AD in the derivatives se978-3-319-53978-2978-3-319-53979-9作者: Criteria 時(shí)間: 2025-3-25 01:14 作者: 閑聊 時(shí)間: 2025-3-25 05:29 作者: Exclude 時(shí)間: 2025-3-25 10:15 作者: SHRIK 時(shí)間: 2025-3-25 13:39 作者: 發(fā)酵劑 時(shí)間: 2025-3-25 19:42
Deploy Machine Learning Models to Productioncredit curves and vega to interpolated volatility surfaces or cubes. The bucketed delta is usually more computationally costly as the multiple curves in a multi-curve framework are often made of 20–100 points and there are 20–100 deltas to compute.作者: Terrace 時(shí)間: 2025-3-25 20:19 作者: GNAT 時(shí)間: 2025-3-26 03:55
Book 2017ible way,?.Algorithmic Differentiation Explained.?will take readers through all the major applications of AD in the derivatives setting with a focus on implementation..Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and data assi作者: 總 時(shí)間: 2025-3-26 08:07
8樓作者: Fibrinogen 時(shí)間: 2025-3-26 08:56
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9樓作者: 善變 時(shí)間: 2025-3-27 03:49
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