Questions tagged [conjugate-gradient]
A popular krylov subspace method for solving linear systems of equations, particularly those that exhibit symmetric positive definiteness.
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Subspaces for Iterative methods
In the original paper of Conjugate Gradients, the authors mention that if we pick the canonical basis $\{e_1,e_2,\ldots,e_n\}$, to obtain A-orthonormal vectors, we end up with the Gaussian elimination ...
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2answers
48 views
What's the difference between the 2 ways of definitions of function handle? which is robust and better?
Recently, I have been studying Krylov subspace iterative methods. I find the matlab robust command pcg and the new concept of the function handle to return a matrix-vector product. Then I use help pcg ...
1
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1answer
31 views
Accelerating Conjugate Gradients fitting for small localized kernel (like cubic B-spline)
Question:
Is there some pre-conditioner for Conjugate-Gradient (CG) cheap enough, that it is worth using even if my operator is very local (i.e. already has a low number of non-zero elements), as it ...
1
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1answer
31 views
Why the iteration steps become twice if the step size reduces half for CG methods?
For CG method for SPD matrices, (Ax = b arising from Poisson equation with homogeneous boundary condition) we know that the convergence theorem:
After m steps of iteration, the error $e^{(m)}=x-x_m$ ...
3
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1answer
48 views
Conjugate Gradient for singular 2D poisson finite element with Neumann Boundary Conditions
Heavily edited question after I realised partly what the problem was
I have programmed a simple 2D square finite element solution to the Poisson equation
$-\Delta u = f$
The source function ...
6
votes
0answers
61 views
Why not use the preconditioned residual as termination criterion for preconditioned CG?
I have a Poisson equation with wildly varying material parameters (1 .. 1000), wildly varying element sizes (5 nm .. 100 um) and some quite anisotropic (tetrahedral) elements (100 nm x 100 um). I use (...
2
votes
1answer
49 views
Blowup of error in Conjugate Gradient method with periodic Dirichlet Poisson matrix
My problem is that the L2-Norm of the residual for the periodic Poisson matrix $P$ is initially decreasing but starts to blow up after a certain number of iterations. The blowup happens earlier the ...
2
votes
1answer
71 views
Nonlinear conjugate gradient with orthogonality constraint
I have to solve a set of nonlinear optimization problems in the subspace defined as the orthogonal space to a given vector.
More precisely,
$$
\arg\min f(\vec x) \qquad \text{with} \qquad \vec x \...
2
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0answers
79 views
Richardson's Iteration, Gradient Method and Spectral Radius
Richardson's iteration introduce a scalar $\alpha$ to the update formula:
$$ \textbf{x}^{(k+1)} = \textbf{x}^{(k)} + \alpha \textbf{r}^{(k)} $$
And compute $\alpha$ by minimizing the spectral radius:...
1
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1answer
90 views
Conjugate gradient - ill-conditioning and numerical tolerance
I would like to solve system $Ax=b$, where $A$ is SPD, but very ill-conditioned ($\text{cond}(A)>10^{11}$). I am interested in using UNpreconditioned version of the conjugate gradient method.
Is ...
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1answer
105 views
Relation between conjugate gradient method and finite elements method
What is difference beetwen this two method? Are these methods far from each other or are these methods complement each other? Could you take an example?
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1answer
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Which C++ linear algebra library is probably the fastest on solving huge sparse [square matrix] linear system?
I am developing a 2D CFD solver for fluid-particle interaction. To solve Navier-Stokes equations on a grid of size $10000\times 10000$ cells (or >1 million cells), a large linear system $Ax=b$ with $A$...
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144 views
How to find a good preconditioner to the system $(A^T A + \lambda I) x = A^T b$?
The system in the title has a damper factor $\lambda > 0$ and the matrix $A$ is sparse and rectangular, with a structure I can exploit to solve matrix vector products very fast. My current solver, ...
4
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0answers
34 views
Stochastic conjugate directions to improve convergence in narrow valleys
My question concerns a specific statement in this paper:
N. N. Schraudolph and T. Graepel, "Conjugate Directions for Stochastic Gradient Descent," in Int. Conf. Artificial Neural Networks, Berlin, ...
3
votes
1answer
129 views
How can a CG solver solve a non positive definite sparse matrix
I am using the CUSP CG solver and I ran it on couple of sparse matrices from the University of Florida sparse matrix collection. The solver was able to solve non positive definite sparse matrices. My ...
6
votes
1answer
250 views
What is required of the objective function in order to use Gauss Newton method?
From what I understand, the Gauss-Newton method is used to find a search direction, then the step size, etc., can be determined by some other method.
In addition to that, are the following ...
3
votes
1answer
318 views
Linear constraints for L-BFGS-B
I know L-BFGS-B only supports simple box constrains of the form: $l_i \leq x_i \leq u_i$, where $l_i$ and $u_i$ are constants. For my specific optimization problem, I need to specify some simple ...
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1answer
63 views
Adaptive gradient descent
I want to minimize some multivariable function $\Delta(\alpha, \beta)$. I know that this function has a zero point, $\Delta(5, 5) = 0$.
Starting from some $(\alpha, \beta)$ close to $(5,5)$ (e.g. (4....
0
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1answer
143 views
How to verify solution to pre-conditioned linear systems solver?
I am solving Ax=b. A has a very large condition number (> O(10^10))
I am using the conjugate gradients method with point jacobi pre-conditioning. I obtained a solution 'x' that "looks" reasonable. ...
7
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1answer
3k views
What are the differences between the different gradient-based numerical optimization methods?
I am interested in the specific differences of the following methods:
The conjugate gradient method (CGM) is an algorithm for the numerical solution of particular systems of linear equations.
The ...
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1answer
159 views
Computing preconditioner for a non-linear conjugate gradient implementation
Consider the following steps for the $i$-th non-linear conjugate gradient iteration, in the context of 3D electromagnetic inversion, and as discussed in (Newman and Boggs, 2004):
(1) set $i = 1$, ...
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0answers
154 views
Search direction for CG method
I am studying optimization methods and I was able to understand and derive the search direction
$$ p_k = r_{k-1} + \beta p_{k-1} $$
for Conjugate Gradient Method, with
$$ \beta = -\frac{p_{k-1}^...
4
votes
2answers
966 views
Conjugate gradient method to minimize a function
I am having some serious difficulties trying to understand how to use (apply) CG to minimize a function. In all the textbooks and notes, the step size $\alpha$ is give by the following expression
$$ \...
2
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0answers
98 views
Optimization based integration for MPM
I'm considering implementing (just for simplicity) the unconstrained implicit optimization based integration for Material Point Method as described in Chenfanfu Jiang's thesis on MPM (the minimization ...
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0answers
2k views
Understanding MATLAB's `fmincg` optimization function
I'm researching numerical optimization. Recently I've come across a variant of a conjugate gradient method named fmincg.
The function is written in MATLAB and is ...
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1answer
2k views
Solving the Poisson equation with Neumann Boundary Conditions - Finite Difference, BiCGSTAB
I am trying to solve the Poisson equation in a rectangular domain using a finite difference scheme with a rectangular mesh.
I have happily generated the matrix system of equations Ax = b which is ...
3
votes
1answer
139 views
CG question: is symmetry always necessary?
Consider the 1D Poisson equation
$$\nabla^2 u = f.$$
Using finite difference method on cell corner data and a uniform grid with ghost points, I think we can write the system of equations with Neumann ...
1
vote
2answers
726 views
Boundary conditions in conjugate gradient method for poisson's equation
I want to use the conjugate gradient method to solve poisson's equation in an electrostatic setup:
\begin{align}
\rho=-\nabla^2\phi
\end{align}
I am however a little confused when it comes to the ...
9
votes
1answer
1k views
Adaptive gradient descent step size when you can't do a line search
I have an objective function $E$ dependent on a value $\phi(x, t = 1.0)$, where $\phi(x, t)$ is the solution to a PDE. I am optimizing $E$ by gradient descent on the initial condition of the PDE: $\...
3
votes
2answers
303 views
2D Laplace problem with mixed boundary conditions using Conjugate Gradients
I am being asked for one of my classes to solve 2D Laplace equations with mixed boundary conditions using the Conjugate Gradient method. The equations and
conditions are given as:
$$
\frac{\partial^...
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1answer
97 views
The linear system in Quasi Newton method
I have implemented a Quasi Newton method for my problem, where I use the Hessian matrix approximation based approach. Hence, there is a linear system solve in every iteration. I solve the linear ...
5
votes
1answer
348 views
Solving a set of linear equations with block structure and weak coupling
I have a standard set of linear equations $Ax=b$ where the Hessian matrix $A$ has the special block structure as shown:
$A=
\begin{pmatrix}
T & U\\
U^T & V
\end{pmatrix}$,
$x=
\begin{...
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1answer
69 views
Conjugate Gradient, initial direction set to initial residual
In the (iterative) Conjugate Gradient (CG) algorithm: https://en.wikipedia.org/wiki/Conjugate_gradient_method
The initial search direction $p_{0}$ is set to the initial residual $r_{0}$. But I can't ...
4
votes
0answers
205 views
Nonlinear conjugate gradient restart threshold 1/10
Nocedal and Wright
on Conjugate Gradient Methods, p. 123, describe a
restart strategy ... whenever two consecutive gradients are far from orthogonal
$\qquad {{| \nabla f_k^T \ \nabla f_{k-1} |}
\...
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1answer
111 views
Can this equation be solved with the conjugate gradient method?
Let $A$ be positive-definite and $C$ diagonal positive-definite, consider the problem of solving the following equation for $\bf x$
$$A{\bf x}+C\begin{bmatrix}
e^{x_1} \\
\vdots \\
e^{x_n}
\end{...
9
votes
2answers
1k views
What is the worst case complexity of Conjugate Gradient?
Let $A\in \mathbb{R}^{n\times n}$, symmetric and positive definite. Suppose it takes $m$ units of work to multiply a vector by $A$. It is well known that performing the CG algorithm on $A$ with ...
8
votes
2answers
273 views
Why does conjugate gradient work with this nonsymmetric preconditioner?
In this previous thread the following multiplicative way to combine symmetric preconditioners $P_1$ and $P_2$ for the symmetric system $Ax=b$ was suggested:
\begin{align}
P_\text{combo}^{-1} :=& ...
3
votes
0answers
108 views
Conjugate gradient: the 1-norm of the residual
I am trying to solve $Ax=b$ using the conjugate gradient method. However, it is important to me to obtain a bound not only on the usual residual $||b-Ax_k||_2$ but also on the quantity $||b-Ax_k||_1$. ...
2
votes
1answer
129 views
Solve FEM matrix from coupled system
I'm developing an FEM solver for a coupled system. I have diffusion and potential equations which result in positive definite matrices for each equation, but the coupling makes the overall system ...
3
votes
0answers
89 views
How to implement conjugate gradient method to minimize this nonlinear action?
Given a 2D stochastic differential equation:
\begin{align}
\dot{x}_{i}=f_{i}(\textbf{x})+g_{ij}\xi_{j}(t),
\end{align}
where $i=2$, $g_{ij}g_{jk}=2\epsilon\delta_{ik}$ and $f(\textbf{x})=-\nabla\phi(\...
3
votes
1answer
209 views
Trust-region Newton: implementation issue with Conjugate Gradient calculations
UPDATE: The problem turned out to be the step (refer penultimate paragraph below) where I was factoring out a small value from the vectors of the numerator and denominator and then computed dot ...
2
votes
1answer
442 views
Hessian-free and Truncated Newton methods
In this paper on Deep Learning for Machine Learning, the approach is referred to as Hessian-free method. That is because the Hessian is never computed explicitly. Instead, the product of the Hessian ...
6
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1answer
339 views
Caveats of Hessian free method
Hessian free iterative optimization techniques like Newton-CG, do not explicitly compute the Hessian but instead approximate the product of the Hessian with a vector through finite difference. The ...
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0answers
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Search Direction in Conjugate Gradient
Could you help me with a Conjugate Gradient question? In using CG to solve Ax=b, why is the search direction $p_{k+1}$ in CG chosen as a linear combination of the residual $r_k$ and previous direction ...
4
votes
0answers
240 views
Is it possible to predict the null space of a structure from contributing elements null spaces?
I am trying to solve an almost incompressible problem with heterogeneous properties by domain decomposition. Solution with CG converges slowly or divergerces completely. My problem becomes ill-...
3
votes
2answers
5k views
What are some reasons that Conjugate Gradient iteration does not converge?
I would greatly appreciate it if you could share some reasons the Conjugate Gradient iteration for Ax = b does not converge? My matrix A is symmetric positive definite.
Thank you so much!
Edit with ...
5
votes
1answer
886 views
Solving a system of nonlinear PDEs by minimization
I have two coupled nonlinear partial differential equations of the form:
$
\begin{align}
\dot{u} -f(u,u',u'',v,v',v'')=0 \\
\dot{v} -g(u,u',u'',v,v',v'')=0
\end{align}
$
The boundary conditions are ...
4
votes
1answer
305 views
Linear equation system: Direct solver works, iterative solver does not
I have to solve for x in b = A*x, where a is sparse. This works fine with Matlab's mldivide: x = A \ b. Since I will have to use an iterative algorithm for very large A, I'm currently testing Matlab's ...
1
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2answers
241 views
SLATEC rouitne dslucs() and MKL correspondence
I am looking for a routine (or set of routines) in the Intel MKL that that can replace dslucs (Incomplete LU BiConjugate Gradient Squared Ax=b Solver) in ...
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2answers
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What's the difference between conjugate gradient method and biconjugate gradient method
What's the difference between these two methods? Can a problem be solved by one method will be able to solved by the other? Can both/or one of them be parallelized with OpenMP and/or MPI?
