Images, posts & videos related to "Adjoint Method"
Hi all, I am looking at an optimisation problem where I want to optimise a geometry using the adjoint method. Physically I'm solving Maxwell's equations around a geometry using FDTD. My question is I more or less understand that you can write Maxwell's equations as a linear set of differential equations but what I don't understand is how you physically interpret what the adjoint of that linear equation is doing to the system. I've read a few papers that claim the adjoint system is just solving the backwards propagation of the original system. But I don't see why that would be the case. I was wondering if someone could give a sort of intuitive idea of what an adjoint does to a system.
Sorry if question is broad and vague, that's mostly because I'm still a little confused by it.
Hello everyone,
I have a masters in mechanical engineering and followed some courses related to numerical methods and optimizations. Having participated to a lecture on optimization, I stumbled upon the adjoint optimization method which caught my attention.
From what I understood, the discrete adjoint optimization method allows you to optimize very complex problems using softwares that already exist. For example, adjoint discrete method is used to perform shape (topology) optimization of e.g. airfoils using CFD results without the need to modify the CFD solver in itself.
So I would like to learn and understand the adjoint method. My idea would be to apply the method to e.g. the shape optimization of a trebuchet. Indeed, I can easily obtain and solve the trebuchet equations and the objective is straightforward : optimize the few design variables so that the thrown distance is maximized. Obviously I know I don't need the adjoint method in such a case since I imagine one can derive and solve the issue analytically. But it would be interesting to practice on an "easy" toy problem.
Could some of you provide me references/books/tutorials/MOOC to reach this objective ?
Thanks !
Hi everyone, you might be interested in our ICML2020 paper if you are doing Neural ODE or time-series modeling.
link to paper: https://arxiv.org/abs/2006.02493
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Results on Cifar image classification
https://preview.redd.it/kzyabmbq7b551.png?width=1844&format=png&auto=webp&s=ae6fb145df62a08ad86829e93b3c88bbb65b80ee
Demo of numerically solving a three-body problem (unknown mass) with ACA for parametric ODE
https://reddit.com/link/ha8u7h/video/p2w82k4t7b551/player
Hi, sharing with my slides and notebooks on NeuralODE. During my talk I put stress on explaining what are ordinary differential equations, how to solve them numerically (how to implement simple black box solver), how to integrate ODE when problem function is given by Neural Network, how to compute gradients with adjoint method vs naive approach. Finally, what are Continuous Normalizing Flows derived in the paper.
Link to repo: github/2019-03-Neural-Ordinary-Differential-Equations
In the repo you can find:
Processing img pjoslv7xrto21...
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EventProp has recently showed up on arXiv as a method to compute exact gradients for Spiking Neural Networks, enabling easier training with gradient methods. I gave a shot at implementing it in PyTorch and seeing how it performs on MNIST (the original paper only presents experimental results on synthetic data) -- here are a few results:
Github: https://github.com/lolemacs/pytorch-eventprop
Paper: https://arxiv.org/abs/2009.08378
Abstract:
We derive the backpropagation algorithm for spiking neural networks composed of leaky integrate-and-fire neurons operating in continuous time. This algorithm, EventProp, computes the exact gradient of an arbitrary loss function of spike times and membrane potentials by backpropagating errors in time. For the first time, by leveraging methods from optimal control theory, we are able to backpropagate errors through spike discontinuities without approximations or smoothing operations. As errors are backpropagated in an event-based manner (at spike times), EventProp requires storing state variables only at these times, providing favorable memory requirements. EventProp can be applied to spiking networks with arbitrary connectivity, including recurrent, convolutional and deep feed-forward architectures. While we consider the leaky integrate-and-fire neuron model in this work, our methodology to derive the gradient can be applied to other spiking neuron models. We demonstrate learning using gradients computed via EventProp in a deep spiking network using an event-based simulator and a non-linearly separable dataset encoded using spike time latencies. Our work supports the rigorous study of gradient-based methods to train spiking neural networks while providing insights toward the development of learning algorithms in neuromorphic hardware.
N
... keep reading on reddit β‘Question:
A manufacturer produces 3 types of candies, namely, A, B, and C. The ingredients used for these candies are grapes, mangoes and strawberries. To produce one unit of A requires 4 grams of grapes, 2 grams of mangoes and 3 grams of strawberries. To produce one unit of B requires 3 grams of grapes and 4 grams of mangoes and 3 grams of strawberries. To produce one unit of C requires 1 grams of grapes, 4 grams of strawberries and 5 grams of mangoes. The manufacturer has an everyday supply of 250 kg of grapes, 450 kg of mangoes and 380 kg of strawberries. Let a, b, and c be the quantity of A, B and C (in thousand units) respectively.
a. Form a system of linear equations from the information above.
(equation 1) 4a + 3b + c = 250 (equation 2) 2a + 4b + 5c = 450 (equation 3) 3a + 3b + 4c = 380
b. Write a matrix equation for the system of linear equations obtained in (a).
AX=B
4 3 1 a 250 ( 2 4 5) ( b ) = ( 450 ) 3 3 4 c 380
[A] top: 4 3 1 middle: 2 4 5 bottom: 3 3 4
[X] top: a middle: b bottom: c
[B] top: 250 middle: 450 bottom: 380
Ρ. Use Gauss Elimination method to find the inverse matrix, hence, find the quantity of A, B and C (in thousand units).
adjoint matrix:
4 3 1 ( 2 4 5 ) 3 3 4
top: 4 3 1 middle: 2 4 5 bottom: 3 3 4
cofactor matrix:
1 7 -12 ( -9 13 -3 ) 11 -18 10
top: 1 7 -12
middle: -9 13 -3
bottom: 11 -18 10
matrix determinant: 247 *using adjoint matrix= |A| = a11 |c11| + a12 |c12| + a13 |c13|
inverse matrix:
A^-1 = 1 4 3 1 β ( 2 4 5 ) 247 3 3 4
A^-1 = 1 Γ· 247 ( 431 245 334 )
top: 4 3 1 middle: 2 4 5 bottom : 3 3 4
**when they asked βLet a, b, and c be the quantity of A, B and C (in thousand units)β does that mean changing the numbers from gram into kg? like 4000g = 4kg?
EDIT: reddit wouldn't let me write in the correct format now it's all confusing :(
As title. I have a page that does nothing, other than output get_included_files(), and it's showing me nearly 100 files from my vendor directory:
Hi all,
I've been working on a physics peoject which uses SGD to find global minima of a potential function(a landscape, essentially). So far, the limited code works okay, but i want to extend and systematize it. I have a bunch of complex variables(matrices) which i'd like to define as classes, or rather, as subclasses of the tf.linalg.LinearOperator, so that I inherit the methods(adjoint, inverse, is_diagonal, etc. ) while also retaining the high-performance aspects of the library.
Problem is, I am not that well versed in writing subclasses, and can't make sense of the docs of the other subclasses. Could someone point me to a minimal implementation of it? Any kind of advice would be appreciated. Also let me know if it is pointless, and i can achieve all that by just simply defining a matrix object in tf.
We have recently developed an Adjoint based Neural ODE (ANODE) which computes unconditionally accurate gradients for Neural ODEs. This is very important as the approach presented in arxiv:1806.07366 is numerically unstable and may result in divergent training (in several cases we observed >20% accuracy degradation because of this)
Link to Pytorch code:
https://github.com/amirgholami/anode
Link to papers:
https://arxiv.org/pdf/1902.10298.pdf
https://arxiv.org/pdf/1906.04596.pdf
We hope this library would be helpful. Please let us know if you have any feedback and feel free to reach out if there was any questions
Update: There have been some questions whether using adaptive solvers can address the instability of solving ODEs backwards in time. Please kindly note that this is incorrect. Using an adaptive solver cannot resolve instability of solving an unstable ODE backwards in time. Please see Figure 7 of our paper which clearly shows an adaptive solver cannot address instability. This claim stems from a misunderstanding of how adaptive solvers work. Below we provide a detailed explanation which we hope would address this misunderstanding:
To solve an ODE numerically we need to choose a step size. ODEs can either be solved with a fixed step size or an adaptive step size. Fixed step sizes use the same step size in every iteration, whereas adaptive methods change the step size dynamically. Ideally, we want to use a large step size so we can solve the ODE with the smallest number of iterations. However, the final error depends on the step size. Using a large step size can lead to a large error. An alternative approach is to use an adaptive solver. Adaptive solvers change the step size dynamically to control error associated with step size. Please note this is irrelevant to making an ODE which is unstable for any step size stable, which is what the Neural ODE paper's algorithm claims. Please note that this is incorrect. That is one cannot solve an unstable ODE and make it stable with an adaptive solver. For example, a famous adaptive solver is RK45. This solver first computes the solution of one time step, with RK4 which is a fourth order method, and RK5 which is a fifth order method. If the step size is chosen properly, the error between these two solutions should be small. Adaptive solvers, measure this err
... keep reading on reddit β‘This was done by Pascal Voitot (@mandubian) . He implemented tensorflow version of NeuralODE. I have only gone through the project and not tried it myself.
https://github.com/mandubian/neural-ode
I am nearing the end of my second semester and I have never felt like I have been given any "interesting" problems. The exercises in books, the homework problems, the readings etc. all focus on building a unified theory of whatever subject I am learning. I am not saying this is easy, a lot of problems can be very challenging and that's fine but everything feels very dry. I am talking about problems like "Show that if T is a normal operator in a complex vector space V, then there exists a p in R[x] with p(T) = T^* and deg(p) = dim(V) - 1, where T^* denotes the adjoint of T." The solution requires a simple use of the spectral theorem. Of course, this shows me an application of the spectral theorem and thus helps justify(?) its existence as a useful problem solving method but I don't see the point of the problem. Of course my feelings aren't specific to linear algebra.
On the other hand I imagine a high school student asking similar questions for subjects he is learning that I might find completely relevant and motivated.
With all that said, I don't blame the mathematics but there is an apparent lack of motivation for subjects and exercises. Certain books like Hadlock's "Field Theory and Its Classical Problems" and Simmons' "Differential Equations with Applications and Historical Notes" successfully propose interesting problems of historical interest and use them to motivate the subject but needless to say they are rare and most of the time I am still left wondering what might the purpose of a particular exercise be? At this point I only ever do them to test my understanding.
So, to cut it short I have two questions.
Do I have a false perspective, am I failing to do something right?
Is there anything I can do about it or do certain things only make sense after having learned even more?
Any advice and comment is appreciated.
EDIT: Could a mod correct my mess in the topic name?
This is a long one... bear with me. If you will.
I am going into the second year of a Computer Science degree and we have a course called "Engineering Mathematics" (ME3) in the next semester.
I graduated high school a WHILE ago and honestly need a brushing up before I start learning ME3. But I don't have the time to go through all the maths topics we had then in all the 4 years. I was wondering if someone could help me decide what I should revisit and revise before going on to ME3.
------------------------------------
1 - Linear Differential Equations (LDE)
\- LDE of nth order with constant coefficients
\- Method of variation of parameters
\- Cauchy's & Legendre's LDE
\- Simultaneous & Symmetric Simultaneous DE
\- Modelling of Electric Circuits
2 - Transforms
\- Fouriers Transform
\- Complex exponential form Fourier series
\- Fourier Integral Theorem
\- Fourier Sine & Cosine Integrals
\- Fourier Sine & Cosine transforms & their inverses
\- Z Transform (ZT)
\- Standard Properties
\- ZT of standard sequences & their inverse
3 - Statistics
\- Measures of Central tendency
\- Standard deviation,
\- Coefficient of variation,
\- Moments, Skewness and Kurtosis
\- Curve fitting: fitting of straight line
\- Parabola and Related curves
\- Correlation and Regression
\- Reliability of Regression Estimates.
4 - Probability and Probability Distributions
\- Probability, Theorems on Probability
\- Bayes Theorem,
\- Random variables,
\- Mathematical Expectation
\- Probability density function
\- Probability distributions: Binomial, Poisson, Normal and Hypergometric
\- Test of Hypothesis: Chi-Square test, t-distribution
5 - Vector Calculus
\- Vector differentiation
\- Gradient, Divergence and Curl
\- Directional derivative
\- Solenoid and Irrigational fields
\- Vector identities. Line, Surface and Volume integrals
\- Greenβs Lemma, Gaussβs Divergence theorem and Stokeβs theorem
6 - Complex Variables
\- Functions of Complex variables
\- Analytic functions
\- Cauchy-Riemann equations
\- Conformal mapping
\- Bilinear tA multidisciplinary, multi-objective "launch vehicle optimizer", including:
And a point-design, 500 kg payload to a 3000 km range vehicle, including but not limited to:
I'm taking offers, BTC only. Material would be ITAR classified if you were in the US, but this throaway account should be free to export it for $$$ . Pm for offers.
Thank you
Scud29
/s
LREM et quelques alliΓ©s.
Je vous laisse googler les dΓ©tails pour chaque affaire. La plupart sont regroupΓ©es dans cet article.
-Bruno Le Maire, Eure, 1
Sa femme employΓ©e comme assistante parlementaire probablement fictive.
-Richard Ferrand, Finistère, 6
Vous Γͺtes au courant de l'affaire.
-Marielle de Sarnez, Paris, 11
Suspicions d'assistants parlementaires europΓ©ens travaillant pour le parti MODEM Γ Paris.
-Bruno Bonnell, RhΓ΄ne, 6
Selon Mediapart, l'entreprise Infogrames dont il a Γ©tΓ© directeur gΓ©nΓ©ral a Γ©copΓ© d'une sanction de 40.000 euros devant l'AutoritΓ© des marchΓ©s financiers. Il est aussi pointΓ© du doigt pour avoir domiciliΓ© des sociΓ©tΓ©s dans le Delaware, Β«petit Γtat amΓ©ricain considΓ©rΓ© comme un paradis fiscalΒ» selon LibΓ©.
-Claire O'Petit, Eure, 5
Celle qui dΓ©ment avoir sollicitΓ© une investiture du Front national aux Γ©lections rΓ©gionales a Γ©tΓ© interdite par le tribunal de Bobigny de Β«diriger, gΓ©rer, administrer ou contrΓ΄ler directement toute entreprise commerciale ou artisanale, toute exploitation agricole ou toute personne morale (β¦) pour une durΓ©e de cinq ansΒ», selon Mediapart.
-Pierre CabarΓ©, Haute-Garonne, 1
Son investiture a été suspendue par La République en marche après les révélations sur son inéligibilité entre 2003 et 2004. Sauf que le parti n'a envoyé personne à sa place et il a continué à faire campagne avec des documents ou figuraient le nom En marche... Il est noté comme "REM" sur le site du ministère de l'intérieur donc on va voir s'ils lui retirent son étiquette ou pas.
-Corinne Vignon, Haute-Garonne, 3
Elle fait l'objet d'une enquΓͺte ouverte par le parquet de Toulouse qui prΓ©cise Γ BuzzFeed News que Corine Vignon aurait Β«exercΓ© une activitΓ© d'astrologie non dΓ©clarΓ©eΒ».
-Alain PΓ©rΓ©a, Aude, 2
Selon Le Midi libre, il a Γ©tΓ© Γ©pinglΓ© en 2010 par la Chambre rΓ©gionale des comptes de Languedoc-Roussillon. Alain PΓ©rΓ©a aurait Γ©tΓ© payΓ© pour 6 mois de travail, soit 9000 euros, pour la rΓ©daction d'une Γ©tude sur les Harkis et le logement qui n'a jamais Γ©tΓ© publiΓ©e.
-Buon Tan, Paris, 9
Une enquΓͺte prΓ©liminaire a Γ©tΓ© ouverte sur le candidat, aprΓ¨s la plainte de la vice-prΓ©sidente du conseil reprΓ©sentatif des associations asiatiques de
... keep reading on reddit β‘I live in a small town in south East France and since several years now a facebook account has become a kind of poison-pen letter writer/ Blackmailer/ Whistle blower against the current mayor and overall any local government. She or he took the name of a "famous" noble lady who died in the late middle ages and publish photo, video, statement that compromise the mayor and his adjoint. I found it very interesting that she (I will say she because she assume a Woman's name) is still online. Even if she has often a point, she often use method that are far too aggressive and personal to my taste. I wonder who she is. That's what I wanted to share with you. What do you think of it ?
Say I want to work on this light transport method as explained in the thesis of Eric Veach. Glancing through it, I see some operator formalism, self-adjoint operator explanation of light transport, path-space manifolds etc.
I have some grasp of manifolds but I really know nothing about functional analysis and advanced abstract linear algebra. What would be the prerequisites for understanding this thesis to the nuts and bolts details.
[Here is the thesis] (http://graphics.stanford.edu/papers/veach_thesis/)
I am looking for companies that actually use Formal methods. Do you know companies absent in a list? The ones I've found so far are:
| Name | Location | Hiring | Sector | Source | Remote OK? |
|---|---|---|---|---|---|
| Adjoint | Boston, MA, USA | No | Finance | Github | |
| Cog Systems | - | - | - | Blog | ? |
| Ethereum | Switzerland | - | - | Blog | ? |
| Galois | Portland, Oregon, USA | Yes | Consulting/Research | ? | |
| Kernkonzept | Germany | - | - | Site | ? |
| Kaspersky Lab | Moscow, Russia | No | Security/AV | ? | |
| Microsoft | Redmond, USA | - | Software development | Site | ? |
| Spotify | - | - | - | Slides | ? |
| Rockwell Collins | USA, Cedar Rapids, Iowa | - | High Assurance Systems | - | ? |
| Trust in soft | - | - | - | Site | ? |
| Trustworthy Systems | - | - | - | Site | ? |
| JetBrains | Saint Petersburg, Russia | - | - | Site | ? |
Latest version of the list is here - https://gist.github.com/ligurio/4b8a647d9474ab90049a6b56c8c731e0
My question is very simple. Given the adjoint of L (not to be confused with the formal adjoint of L), which in turn is given by the formal adjoint L+ such that the bilinear concomitant associated with L is 0, it is evident that if L acting on some function, call it Ο, is a non homogeneous boundary value problem, say L[Ο] = h(ΞΆ), Ο(ΞΆ ) has a solution if, the adjoint exists, then there must be some function, ΞΎ(ΞΆ) that satisfies the boundary conditions that are a result of the bilinear concomitant associated with L being zero if and only if, the inner product of ΞΎ(ΞΆ) with h(ΞΆ), (of course with respect to some weight function, call it Ο(ΞΆ)), is zero; that is to say, given ΞΎ(ΞΆ) and h(ΞΆ) are orthogonal with respect to Ο(ΞΆ), there exists a solution to the non homogeneous, linear differential equation given by LΟ. Obviously this solution is not clearly evident and may not even have a closed form, with that being said, could one not simply use a modified Fourier-Hankel method to solve the system coupled of differential equations given by LΟ and L'π to obtain a solution by choosing the appropriate weight function, Ο(ΞΆ), such that the inner product of h(ΞΆ) and the inner product of h'(ΞΆ) has an analytic solution? It seems obvious to me that this would yield a much more useful and applicable result.
Addendum: I know this is fairly mathematical, but this is an elementary problem that I feel not only has obvious repercussions in applied fields, but could be understood and intuited by any modern human.
I just finished an introductory course to partial differential equations. We covered how to solve homogeneous problems by separation of variables and nonhomogeneous problems with the method of eigenfunction expansion.
At the heart of the course was solving the Sturm Liouville (S-L) eigenvalue problem to get your eigenvalues and eigenfunctions from the boundary conditions.
All semester when we solved the S-L problem we had to show that the differential operator and the boundary conditions made the operator self-adjoint, which I mastered, but the professor never really explained what exactly that tells us about the problem.
So my question is what is self-adjointness and why is it useful/important?
Hey there. I'm having a problem with proving the last step in the spectral theorem. Here's where I'm at:
For a self adjoint operator A, I have constructed a projection valued meassure (i.e., a map P from the Borel sets to the bounded operators on my Hilbert space, P(M) is a projection which additive on disjoint sets).
The projection satisfies for [; \phi, \psi \in H ;], [; \Omega \in B(R);]
[;<\phi,;][;R;]A[;(z)\psi>;]= [;\int_R;][;\frac{1}{\lambda -z};] d[;\mu_{\phi,\psi}(\lambda);]
where [;R;]A (z) is the resolvent of A in the point z, and [;\mu{\phi,\psi} ;] is the unique complex meassure (this is proven earlier) that makes this equation true.
Now I want to prove that
[; <\psi, A\psi>=\int_R \lambda d\mu_{\psi}(\lambda) ;]
where [;\mu_{\psi}=\mu_{\psi,\psi} is non negative.
If it should help, the book I'm using is Mathematical methods in quantum mechanics by G. Teschl.
Okay, I can't get latex to work, each time I try to make a subscrict (fx. R_A) it fucks up -.-.
Sorry this is late, we'll close the voting say.... Thursday.
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It's almost 2020. We are officially living in the future.
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Sorry we're late. I'll close the voting Tuesday, or some other time.
This topic might bleed into January, pending holidays...
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