My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model.
Hierarchical Deterministic Quadrature Methods for Option Pricing under the Ro...Chiheb Ben Hammouda
Conference talk at the SIAM Conference on Financial Mathematics and Engineering, held in virtual format, June 1-4 2021, about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model".
- Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700
Numerical Smoothing and Hierarchical Approximations for E cient Option Pricin...Chiheb Ben Hammouda
My talk at the "Stochastic Numerics and Statistical Learning: Theory and Applications" Workshop at KAUST (King Abdullah University of Science and Technology), May 23, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation".
My talk entitled "Numerical Smoothing and Hierarchical Approximations for Efficient Option Pricing and Density Estimation", that I gave at the "International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022. The talk is related to our recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874) and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708). In these two works, we introduce the numerical smoothing technique that improves the regularity of observables when approximating expectations (or the related integration problems). We provide a smoothness analysis and we show how this technique leads to better performance for the different methods that we used (i) adaptive sparse grids, (ii) Quasi-Monte Carlo, and (iii) multilevel Monte Carlo. Our applications are option pricing and density estimation. Our approach is generic and can be applied to solve a broad class of problems, particularly for approximating distribution functions, financial Greeks computation, and risk estimation.
To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures - a co-authoring network, an airline network, and a social bookmarking system - assessing the relevancy of the triggering pattern mining approach.
Workshop: Numerical Analysis of Stochastic Partial Differential Equations (NASPDE), in Network Eurandom at Eindhoven University of Technology, May 16, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).
In this talk we will describe a methodology to handle the causality to make inference on common-cause failure in a situation of missing data. The data are collected in the form of contingency table but the available information are only the numbers of CCF of different orders and the numbers of failure due to a given cause. Therefore only the margins of the contingency table are observed; thefrequencies in each cell are unknown. Assuming a Poisson model for the count, we suggest a Bayesian approach and we use the inverse Bayes formula (IBF) combined with a Metropolis-Hastings algorithm to make inference on the rate of occurrence for the different combination cause, order. The performance of the resulting algorithm is evaluated through simulations. A comparison is made with results obtained from the _-composition approach to deal with causality suggested by Zheng et al. (2013).
My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)
My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model.
Hierarchical Deterministic Quadrature Methods for Option Pricing under the Ro...Chiheb Ben Hammouda
Conference talk at the SIAM Conference on Financial Mathematics and Engineering, held in virtual format, June 1-4 2021, about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model".
- Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700
Numerical Smoothing and Hierarchical Approximations for E cient Option Pricin...Chiheb Ben Hammouda
My talk at the "Stochastic Numerics and Statistical Learning: Theory and Applications" Workshop at KAUST (King Abdullah University of Science and Technology), May 23, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation".
My talk entitled "Numerical Smoothing and Hierarchical Approximations for Efficient Option Pricing and Density Estimation", that I gave at the "International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022. The talk is related to our recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874) and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708). In these two works, we introduce the numerical smoothing technique that improves the regularity of observables when approximating expectations (or the related integration problems). We provide a smoothness analysis and we show how this technique leads to better performance for the different methods that we used (i) adaptive sparse grids, (ii) Quasi-Monte Carlo, and (iii) multilevel Monte Carlo. Our applications are option pricing and density estimation. Our approach is generic and can be applied to solve a broad class of problems, particularly for approximating distribution functions, financial Greeks computation, and risk estimation.
To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures - a co-authoring network, an airline network, and a social bookmarking system - assessing the relevancy of the triggering pattern mining approach.
Workshop: Numerical Analysis of Stochastic Partial Differential Equations (NASPDE), in Network Eurandom at Eindhoven University of Technology, May 16, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).
In this talk we will describe a methodology to handle the causality to make inference on common-cause failure in a situation of missing data. The data are collected in the form of contingency table but the available information are only the numbers of CCF of different orders and the numbers of failure due to a given cause. Therefore only the margins of the contingency table are observed; thefrequencies in each cell are unknown. Assuming a Poisson model for the count, we suggest a Bayesian approach and we use the inverse Bayes formula (IBF) combined with a Metropolis-Hastings algorithm to make inference on the rate of occurrence for the different combination cause, order. The performance of the resulting algorithm is evaluated through simulations. A comparison is made with results obtained from the _-composition approach to deal with causality suggested by Zheng et al. (2013).
My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)
We present recent result on the numerical analysis of Quasi Monte-Carlo quadrature methods, applied to forward and inverse uncertainty quantification for elliptic and parabolic PDEs. Particular attention will be placed on Higher
-Order QMC, the stable and efficient generation of
interlaced polynomial lattice rules, and the numerical analysis of multilevel QMC Finite Element discretizations with applications to computational uncertainty quantification.
I am Jayson L. I am a Signals and Systems Homework Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, from the University of Sheffield. I have been helping students with their homework for the past 7 years. I solve homework related to Signals and Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems homework.
The word optimal is used in different ways in mesh generation. It could mean that the output is in some sense, "the best mesh" or that the algorithm is, by some measure, "the best algorithm". One might hope that the best algorithm also produces the best mesh, but maybe some tradeoffs are necessary. In this talk, I will survey several different notions of optimality in mesh generation and explore the different tradeoffs between them. The bias will be towards Delaunay/Voronoi methods.
ZK Study Club: Sumcheck Arguments and Their ApplicationsAlex Pruden
Talk given at the ZK Study Club by Jonathan Bootle and Katerina Sotiraki about the universality of sumcheck arguments and their importance in zero-knowledge cryptography.
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter (Lambda), which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter Lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
Practical and Worst-Case Efficient ApportionmentRaphael Reitzig
Proportional apportionment is the problem of assigning seats to parties according to their relative share of votes. Divisor methods are the de-facto standard solution, used in many countries.
In recent literature, there are two algorithms that implement divisor methods: one by Cheng and Eppstein (ISAAC, 2014) has worst-case optimal running time but is complex, while the other (Pukelsheim, 2014) is relatively simple and fast in practice but does not offer worst-case guarantees.
This talk presents the ideas behind a novel algorithm that avoids the shortcomings of both. We investigate the three contenders in order to determine which is most useful in practice.
Read more over here: http://reitzig.github.io/publications/RW2015b
This presentation provides an overview of decision-making in organisations and introduces a new language called ESL that uses actors to create an executable model that can be analysed. A number of small examples of ESL are shown. The presentation concludes with a larger case study that addresses the recent demonetisation event in India.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
We present recent result on the numerical analysis of Quasi Monte-Carlo quadrature methods, applied to forward and inverse uncertainty quantification for elliptic and parabolic PDEs. Particular attention will be placed on Higher
-Order QMC, the stable and efficient generation of
interlaced polynomial lattice rules, and the numerical analysis of multilevel QMC Finite Element discretizations with applications to computational uncertainty quantification.
I am Jayson L. I am a Signals and Systems Homework Expert at matlabassignmentexperts.com. I hold a Master's in Matlab, from the University of Sheffield. I have been helping students with their homework for the past 7 years. I solve homework related to Signals and Systems.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems homework.
The word optimal is used in different ways in mesh generation. It could mean that the output is in some sense, "the best mesh" or that the algorithm is, by some measure, "the best algorithm". One might hope that the best algorithm also produces the best mesh, but maybe some tradeoffs are necessary. In this talk, I will survey several different notions of optimality in mesh generation and explore the different tradeoffs between them. The bias will be towards Delaunay/Voronoi methods.
ZK Study Club: Sumcheck Arguments and Their ApplicationsAlex Pruden
Talk given at the ZK Study Club by Jonathan Bootle and Katerina Sotiraki about the universality of sumcheck arguments and their importance in zero-knowledge cryptography.
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter (Lambda), which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter Lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
Practical and Worst-Case Efficient ApportionmentRaphael Reitzig
Proportional apportionment is the problem of assigning seats to parties according to their relative share of votes. Divisor methods are the de-facto standard solution, used in many countries.
In recent literature, there are two algorithms that implement divisor methods: one by Cheng and Eppstein (ISAAC, 2014) has worst-case optimal running time but is complex, while the other (Pukelsheim, 2014) is relatively simple and fast in practice but does not offer worst-case guarantees.
This talk presents the ideas behind a novel algorithm that avoids the shortcomings of both. We investigate the three contenders in order to determine which is most useful in practice.
Read more over here: http://reitzig.github.io/publications/RW2015b
This presentation provides an overview of decision-making in organisations and introduces a new language called ESL that uses actors to create an executable model that can be analysed. A number of small examples of ESL are shown. The presentation concludes with a larger case study that addresses the recent demonetisation event in India.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
1. Journal of Functional Programming, 33(e1), January 2023
Is Sized Typing for Coq Practical?
Jonathan Chan
University of British Columbia,
University of Pennsylvania
Yufeng (Michael) Li
University of Waterloo,
University of Cambridge
William J. Bowman
University of British Columbia
4. Termination Checking: Guardedness
4
Sized Types Contributions Implementation
Fixpoint div n m : nat :=
match n with
| S n′ => S (div minus n′ m m)
| O => O
end.
❌
Fixpoint minus n m : nat :=
match n, m with
| S n′, S m′ => minus n′ m′
| _, _ => O
end.
5. Termination Checking: Guardedness
5
Sized Types Contributions Implementation
Fixpoint div n m : nat :=
match n with
| S n′ => S (div minus n′ m m)
| O => O
end.
❌
Fixpoint minus n m : nat :=
match n, m with
| S n′, S m′ => minus n′ m′
| _, _ => O n
end.
❌
✅
6. Termination Checking: Sized Typing
6
Sized Types Contributions Implementation
Γ ⊢ n : nats+1
Γ ⊢ e1
: P O
Γ ⊢ n : nats
Γ, m : nats
⊢ e2
: P (S m)
───────────── ─────────────── ─────────────────────────
Γ ⊢ O : nats+1
Γ ⊢ S n : nats+1
Γ ⊢ match n with .
| O => e1
| S m => e2
end : P n
s ⩴ v | s+1 | ∞
7. Termination Checking: Sized Typing
7
Sized Types Contributions Implementation
Γ ⊢ n : nats+1
Γ ⊢ e1
: P O
Γ ⊢ n : nats
Γ, m : nats
⊢ e2
: P (S m)
───────────── ─────────────── ─────────────────────────
Γ ⊢ O : nats+1
Γ ⊢ S n : nats+1
Γ ⊢ match n with .
| O => e1
| S m => e2
end : P n
8. Termination Checking: Sized Typing
8
Sized Types Contributions Implementation
Fixpoint minus : natv
-> nat -> natv
.
Fixpoint div (n : natv+1
) (m : nat) : natv+1
:=
match n with
| S n′ => S (div (minus n′ m) m)
| O => O
end.
natv
natv
33. Real Example: MSets/MSetList.v
Mean over five trials
33
Sized Types Contributions
Unsized compilation (s) 15.122 ± 0.073
Sized compilation (s) 83.660 ± 0.286
Slowdown 5.5×
SAT ops only (s) 64.600 ± 0.437
SAT ops only (%) 77.2%
Inference Substitution Performance
34. Real Example: MSets/MSetList.v
34
Sized Types Contributions
log(count)
log distribution of |𝒱| × |𝒞| during SAT operations
|𝒱| × |𝒞|
Inference Substitution Performance
~4K vs. × ~250 cs.
39. Concrete sized naturals
O : natv+1
S O : natv+2
S (S O) : natv+3
O : natv+2
S O : natv+3
S (S O) : natv+4
39
40. Real Example: setoid_ring/Field_theory.v
40
Mean over two trials (August 2023)
Unsized compilation (s) 17.815 ± 0.545
Sized compilation (s) 106.87 ± 1.94
Slowdown 6.0×
SAT ops only (s) 84.70
SAT ops only (%) 79.3% 17755 vs. × 14057 cs. ≈ 250M
41. Artificial Example: Universe Polymorphism
Set Printing Universes.
Set Universe Polymorphism.
Time Definition T1 : Type := Type -> Type
-> Type -> Type -> Type -> Type. Print T1.
Time Definition T2 : Type :=
T1 -> T1 -> T1 -> T1 -> T1 -> T1. Print T2.
Time Definition T3 : Type :=
T2 -> T2 -> T2 -> T2 -> T2 -> T2. Print T3.
Time Definition T4 : Type :=
T3 -> T3 -> T3 -> T3 -> T3 -> T3. Print T4.
Time Definition T5 : Type :=
T4 -> T4 -> T4 -> T4 -> T4 -> T4. Print T5.
Time Definition T6 : Type :=
T5 -> T5 -> T5 -> T5 -> T5 -> T5. Print T6.
Time Definition T7 : Type :=
T6 -> T6 -> T6 -> T6 -> T6 -> T6. Print T7.
41
Definition #u Time (s)
T1 7 ~ 0
T2 43 0.002
T3 259 0.026
T4 1555 0.057
T5 9331 0.374
T6 55987 3.300
T7 335921 18.170