Questions tagged [conjugate-gradient]
A popular krylov subspace method for solving linear systems of equations, particularly those that exhibit symmetric positive definiteness.
2
votes
1answer
65 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,
$$
\arg\min f(\vec x) \qquad \text{with} \qquad \vec x \...
2
votes
0answers
52 views
Richardson's Iteration, Gradient Method and Spectral Radius
Richardson's iteration introduce a scalar $\alpha$ to the update formula:
$$ \textbf{x}^{(k+1)} = \textbf{x}^{(k)} + \alpha \textbf{r}^{(k)} $$
And compute $\alpha$ by minimizing the spectral radius:...
1
vote
1answer
79 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 ...
1
vote
1answer
97 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?
2
votes
1answer
347 views
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$...
8
votes
0answers
118 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, ...
3
votes
0answers
32 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
97 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
220 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 ...
4
votes
1answer
249 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 ...
0
votes
1answer
61 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
votes
1answer
141 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. ...
8
votes
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 ...
1
vote
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$, ...
1
vote
0answers
145 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
806 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
$$ \...
3
votes
0answers
94 views
Optimization based integration for MPM
I'm considering implementing (just for simplicity) the unconstrained implicit optimization based integration for Material Point Method as described in Chenfanfu Jiang's thesis on MPM (the minimization ...
4
votes
0answers
1k views
Understanding MATLAB's `fmincg` optimization function
I'm researching numerical optimization. Recently I've come across a variant of a conjugate gradient method named fmincg.
The function is written in MATLAB and is ...
1
vote
1answer
1k views
Solving the Poisson equation with Neumann Boundary Conditions - Finite Difference, BiCGSTAB
I am trying to solve the Poisson equation in a rectangular domain using a finite difference scheme with a rectangular mesh.
I have happily generated the matrix system of equations Ax = b which is ...
4
votes
1answer
133 views
CG question: is symmetry always necessary?
Consider the 1D Poisson equation
$$\nabla^2 u = f.$$
Using finite difference method on cell corner data and a uniform grid with ghost points, I think we can write the system of equations with Neumann ...
1
vote
2answers
641 views
Boundary conditions in conjugate gradient method for poisson's equation
I want to use the conjugate gradient method to solve poisson's equation in an electrostatic setup:
\begin{align}
\rho=-\nabla^2\phi
\end{align}
I am however a little confused when it comes to the ...
10
votes
1answer
989 views
Adaptive gradient descent step size when you can't do a line search
I have an objective function $E$ dependent on a value $\phi(x, t = 1.0)$, where $\phi(x, t)$ is the solution to a PDE. I am optimizing $E$ by gradient descent on the initial condition of the PDE: $\...
3
votes
2answers
279 views
2D Laplace problem with mixed boundary conditions using Conjugate Gradients
I am being asked for one of my classes to solve 2D Laplace equations with mixed boundary conditions using the Conjugate Gradient method. The equations and
conditions are given as:
$$
\frac{\partial^...
1
vote
1answer
95 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
263 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{...
1
vote
1answer
67 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 ...
5
votes
0answers
186 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} |}
\...
1
vote
1answer
108 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{...
8
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 ...
9
votes
2answers
256 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} :=& ...
4
votes
0answers
101 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
124 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
198 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
420 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
votes
1answer
321 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 ...
1
vote
0answers
150 views
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
228 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
4k 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
820 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
299 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
vote
2answers
232 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 ...
8
votes
2answers
2k views
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?
5
votes
0answers
177 views
Conjugate residual/gradient convergence checking in practice
Let's say we want to solve $Ax=b$ ($A$ symmetric positive /semi/definite) with the conjugate residual/gradient method. $A$ comes from FEM where the mesh is being refined. The exact solution is $x_*$ ...
5
votes
2answers
158 views
Would recalculating the residual in the conjugate gradient method help?
The conjugate gradient method suffers from an accumulation of errors as it continues. For this reason it is unwise to use it as a direct solver. My question is, would it help to recalculate the ...
1
vote
2answers
6k views
How to solve this system with conjugate gradient algorithm in matlab
CG Algorithm
https://skydrive.live.com/redir?resid=E0ED7271C68BE47C!386&v=3
System of equations, the question and the example
https://skydrive.live.com/redir?resid=E0ED7271C68BE47C!387&v=3
<...
2
votes
0answers
74 views
Which non-linear conjugate gradient possess finite termination property
There are many variants of non-linear conjugate gradient method available ( Flatcher-Reeves, Polak-Rebiere, Dai-Yuan). In case of minimization of quadratic function when first search direction is ...
2
votes
1answer
433 views
PETSc Krylov Subspace and nullspace
How is the nullspace correction implemented in the PETSc conjugate gradient solver? I searched the source code and documentation, but could not find references on the actual implementation.
For ...
10
votes
2answers
6k views
Gradient descent and conjugate gradient descent
For a project, I have to implement these two methods and compare how they perform on different functions.
It looks like the conjugate gradient method is meant to solve systems of linear equations of ...
8
votes
2answers
3k views
Genetic algorithm vs conjugate gradient method
I am trying to optimize some force-field parameters in a molecular framework so that the result of simulation comes as close as it can to the experimental structure.
In the past, I have written a ...
