main.tex
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```
%------------------------------- Script de Duiks --------------------------%
% Include package and setting
\input{packageNSetting}
\begin{document}
%========================================= TITRE =====================================
\begin{lstlisting}
MUD2
c
c file mud2.d
c
c * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
c * *
c * copyright (c) 2008 by UCAR *
c * *
c * University Corporation for Atmospheric Research *
c * *
c * all rights reserved *
c * *
c * MUDPACK version 5.0.1 *
c * *
c * A Fortran Package of Multigrid *
c * *
c * Subroutines and Example Programs *
c * *
c * for Solving Elliptic Partial Differential Equations *
c * *
c * by *
c * *
c * John Adams *
c * *
c * of *
c * *
c * the National Center for Atmospheric Research *
c * *
c * Boulder, Colorado (80307) U.S.A. *
c * *
c * which is sponsored by *
c * *
c * the National Science Foundation *
c * *
c * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
c
c ... file mud2.d
c
c contains documentation for:
c subroutine mud2(iparm,fparm,work,coef,bndyc,rhs,phi,mgopt,ierror)
c A sample fortran driver is file "tmud2.f".
c
c ... required MUDPACK files
c
c mudcom.f
c
c ... purpose
c
c subroutine mud2 automatically discretizes and attempts to compute
c the second-order difference approximation to the two-dimensional
c linear nonseparable elliptic partial differential equation on a
c rectangle. the approximation is generated on a uniform grid covering
c the rectangle (see mesh description below). boundary conditions
c may be specified (dirchlet), periodic, or mixed derivative in any
c combination. the form of the pde solved is:
c
c
c cxx(x,y)*pxx + cyy(x,y)*pyy + cx(x,y)*px + cy(x,y)*py +
c
c ce(x,y)*p(x,y) = r(x,y).
c
c
c pxx,pyy,px,py are second and first partial derivatives of the
c unknown real solution function p(x,y) with respect to the
c independent variables x,y. cxx,cyy,cx,cy,ce are the known
c real coefficients of the elliptic pde and r(x,y) is the known
c real right hand side of the equation. cxx and cyy should be
c positive for all x,y in the solution region. Nonseparability
c means some of the coefficients depend on both x and y. If
c the PDE is separable subroutine mud2sp should be used instead
c of mud2 or muh2.
c
c
c ... mesh description . . .
c
c the approximation is generated on a uniform nx by ny grid. the grid
c is superimposed on the rectangular solution region
c
c [xa,xb] x [yc,yd].
c
c let
c
c dlx = (xb-xa)/(nx-1), dly = (yd-yc)/(ny-1)
c
c be the uniform grid increments in the x,y directions. then
c
c xi=xa+(i-1)*dlx, yj=yc+(j-1)*dly
c
c for i=1,...,nx and j=1,...,ny denote the x,y uniform mesh points
c
c
c ... language
c
c fortran90/fortran77
c
c
c ... portability
c
c mudpack5.0.1 software has been compiled and tested with Fortran77
c and Fortran90 on a variety of platforms.
c
c ... methods
c
c details of the methods employeed by the solvers in mudpack are given
c in [1,9]. [1,2,9] contain performance measurements on a variety of
c elliptic pdes (see "references" in the file "readme"). in summary:
c
c *** discretization and solution (second-order solvers) (see [1])
c
c the pde and boundary conditions are automatically discretized at all
c grid levels using second-order finite difference formula. diagonal
c dominance at coarser grid levels is maintained in the presence of
c nonzero first-order terms by adjusting the second-order coefficient
c when necessary. the resulting block tri-diagonal linear system is
c approximated using multigrid iteration [10,11,13,15,16,18]. version
c 5.0.1 of mudpack uses only fully weighted residual restriction. defaults
c include cubic prolongation and w(2,1) cycles. these can be overridden
c with selected multigrid options (see "mgopt"). error control based on
c maximum relative differences is available. full multigrid cycling (fmg)
c or cycling beginning or restarting at the finest grid level can be
c selected. a menu of relaxation methods including gauss-seidel point,
c line relaxation(s) (in any combination of directions) and planar
c relaxation (for three-dimensional anisotropic problems) are provided.
c all methods use ordering based on alternating points (red/black),
c lines, or planes for cray vectorization and improved convergence
c rates [14].
c
c *** higher order solution (fourth-order solvers) (see [9,19,21])
c
c if the multigrid cycling results in a second-order estimate (i.e.,
c discretization level error is reached) then this can be improved to a
c fourth-order estimate using the technique of "deferred corrections."
c the values in the solution array are used to generate a fourth-order
c approximation to the truncation error. second-order finite difference
c formula are used to approximate third and fourth partial derivatives
c of the solution function [3]. the truncation error estimate is
c transferred down to all grid levels using weighted averaging where
c it serves as a new right hand side. the default multigrid options
c are used to compute the fourth-order correction term which is added
c to the original solution array.
c
c
c ... references (partial)
c
c
c [1] J. Adams, "MUDPACK: Multigrid Fortran Software for the Efficient
c Solution of Linear Elliptic Partial Differential Equations,"
c Applied Math. and Comput. vol.34, Nov 1989, pp.113-146.
c
c [2] J. Adams,"FMG Results with the Multigrid Software Package MUDPACK,"
c proceedings of the fourth Copper Mountain Conference on Multigrid, SIAM,
c 1989, pp.1-12.
c .
c .
c .
c [7] J. Adams, R. Garcia, B. Gross, J. Hack, D. Haidvogel, and V. Pizzo,
c "Applications of Multigrid Software in the Atmospheric Sciences,"
c Mon. Wea. Rev.,vol. 120 # 7, July 1992, pp. 1447-1458.
c .
c .
c .
c [9] J. Adams, "Recent Enhancements in MUDPACK, a Multigrid Software
c package for Elliptic Partial Differential Equations," Applied Math.
c and Comp., 1991, vol. 43, May 1991, pp. 79-94.
c
c [10]J. Adams, "MUDPACK-2: Multigrid Software for Approximating
c Elliptic Partial Differential Equations on Uniform Grids with
c any Resolution," Applied Math. and Comp., 1993, vol. 53, February
c 1993, pp. 235-249
c .
c .
c .
c
c ... argument description
c
c
c **********************************************************************
c *** input arguments *************************************************
c **********************************************************************
c
c
c ... iparm
c
c an integer vector of length 17 used to pass integer
c arguments. iparm is set internally and defined as
c follows:
c
c
c ... intl=iparm(1)
c
c an initialization argument. intl=0 must be input
c on an initial call. in this case input arguments will
c be checked for errors and the elliptic partial differential
c equation and boundary conditions will be discretized using
c second order finite difference formula.
c
c *** An approximation is NOT generated after an intl=0 call!
c mud2 should be called with intl=1 to approximate the elliptic
c PDE discretized by the intl=0 call. intl=1 should also
c be input if mud2 has been called earlier and only the
c values in in rhs (see below) or gbdy (see bndyc below)
c or phi (see below) have changed. This will bypass
c redundant pde discretization and argument checking
c and save computational time. Some examples of when
c intl=1 calls should be used are:
c
c (0) after a intl=0 argument checking and discretization call
c
c (1) mud2 is being recalled for additional accuracy. In
c this case iguess=iparm(12)=1 should also be used.
c
c (2) mud2 is being called every time step in a time dependent
c problem (see discussion below) where the elliptic operator
c does not depend on time.
c
c (3) mud2 is being used to solve the same elliptic equation
c for several different right hand sides (iguess=0 should
c probably be used for each new righthand side).
c
c intl = 0 must be input before calling with intl = 1 when any
c of the following conditions hold:
c
c (a) the initial call to mud2
c (b) any of the integer arguments other than iguess=iparm(12)
c or maxcy=iparm(13) or mgopt have changed since the previous
c call.
c
c (c) any of the floating point arguments other than tolmax=
c fparm(5) have changed since the previous call
c
c (d) any of the coefficients input by coef (see below) have
c changed since the previous call
c
c (e) any of the "alfa" coefficients input by bndyc (see below)
c have changed since the previous call.
c
c If any of (a) through (e) are true then the elliptic PDE
c must be discretized or rediscretized. If none of (a)
c through (e) holds, calls can be made with intl=1.
c Incorrect calls with intl=1 will produce erroneous results.
c *** The values set in the saved work space "work" (see below) with
c an intl=0 call must be preserved with subsequent intl=1 calls.
c
c MUDPACK software performance should be monitored for intl=1
c calls. The intl=0 discretization call performance depends
c primarily on the efficiency or lack of efficiency of the
c user provided subroutines for pde coefficients and
c boundary conditions.
c
c ... nxa=iparm(2)
c
c flags boundary conditions on the edge x=xa
c
c = 0 if p(x,y) is periodic in x on [xa,xb]
c (i.e., p(x+xb-xa,y) = p(x,y) for all x,y
c (if nxa=0 then nxb=0 is required, see ierror = 2)
c
c = 1 if p(xa,y) is specified (this must be input thru phi(1,j))
c
c = 2 if there are mixed derivative boundary conditions at x=xa
c (see bndyc)
c
c
c ... nxb=iparm(3)
c
c flags boundary conditions on the edge x=xb
c
c = 0 if p(x,y) is periodic in x on [xa,xb]
c (i.e., p(x+xb-xa,y) = p(x,y) for all x,y)
c (if nxb=0 then nxa=0 is required, see ierror = 2)
c
c = 1 if p(xb,y) is specified (this must be input thru phi(nx,j))
c
c = 2 if there are mixed derivative boundary conditions at x=xb
c (see bndyc)
c
c
c ... nyc=iparm(4)
c
c flags boundary conditions on the edge y=yc
c
c = 0 if p(x,y) is periodic in y on [yc,yd]
c (i.e., p(x,y+yd-yc) = p(x,y) for all x,y
c (if nyc=0 then nyd=0 is required, see ierror = 2)
c
c = 1 if p(x,yc) is specified (this must be input thru phi(i,1))
c
c = 2 if there are mixed derivative boundary conditions at y=yc
c (see bndyc)
c
c
c ... nyd=iparm(5)
c
c flags boundary conditions on the edge y=yd
c
c = 0 if p(x,y) is periodic in y on [yc,yd]
c (i.e., p(x,y+yd-yc) = p(x,y) for all x,y
c (if nyd=0 then nyc=0 is required, see ierror = 2)
c
c = 1 if p(x,yd) is specified (this must be input thru phi(i,ny))
c
c = 2 if there are mixed derivative boundary conditions at y=yd
c (see bndyc)
c
c
c *** grid size arguments
c
c
c ... ixp = iparm(6)
c
c an integer greater than one which is used in defining the number
c of grid points in the x direction (see nx = iparm(10)). "ixp+1"
c is the number of points on the coarsest x grid visited during
c multigrid cycling. ixp should be chosen as small as possible.
c recommended values are the small primes 2 or 3.
c larger values can reduce multigrid convergence rates considerably,
c especially if line relaxation in the x direction is not used.
c if ixp > 2 then it should be 2 or a small odd value since a power
c of 2 factor of ixp can be removed by increasing iex = iparm(8)
c without changing nx = iparm(10).
c
c
c ... jyq = iparm(7)
c
c an integer greater than one which is used in defining the number
c of grid points in the y direction (see ny = iparm(11)). "jyq+1"
c is the number of points on the coarsest y grid visited during
c multigrid cycling. jyq should be chosen as small as possible.
c recommended values are the small primes 2 or 3.
c larger values can reduce multigrid convergence rates considerably,
c especially if line relaxation in the y direction is not used.
c if jyq > 2 then it should be 2 or a small odd value since a power
c of 2 factor of jyq can be removed by increasing jey = iparm(9)
c without changing ny = iparm(11).
c
c
c ... iex = iparm(8)
c
c a positive integer exponent of 2 used in defining the number
c of grid points in the x direction (see nx = iparm(10)).
c iex .le. 50 is required. for efficient multigrid cycling,
c iex should be chosen as large as possible and ixp=iparm(8)
c as small as possible within grid size constraints when
c defining nx.
c
c
c ... jey = iparm(9)
c
c a positive integer exponent of 2 used in defining the number
c of grid points in the y direction (see ny = iparm(11)).
c jey .le. 50 is required. for efficient multigrid cycling,
c jey should be chosen as large as possible and jyq=iparm(7)
c as small as possible within grid size constraints when
c defining ny.
c
c
c
c ... nx = iparm(10)
c
c the number of equally spaced grid points in the interval [xa,xb]
c (including the boundaries). nx must have the form
c
c nx = ixp*(2**(iex-1)) + 1
c
c where ixp = iparm(6), iex = iparm(8).
c
c
c ... ny = iparm(11)
c
c the number of equally spaced grid points in the interval [yc,yd]
c (including the boundaries). ny must have the form:
c
c ny = jyq*(2**(jey-1)) + 1
c
c where jyq = iparm(7), jey = iparm(9).
c
c
c *** example
c
c suppose a solution is wanted on a 33 by 97 grid. then
c ixp=2, jyq=6 and iex=jey=5 could be used. a better
c choice would be ixp=2, jyq=3, and iex=5, jey=6.
c
c *** grid size flexibility considerations:
c
c the hybrid multigrid/direct method code muh2 provides more grid size
c flexibility than mud2 by removing the constraint that ixp and jyq are
c 2 or 3. This is accomplished by using a direct method whenever the
c coarsest (ixp+1) X (jyq+1) grid is encountered in multigrid cycling.
c If nx = ixp+1 and ny = jyq+1 then muh2 becomes a full direct method.
c muh2 is roughly equivalent to mud2 in efficiency as long as ixp and
c jyq remain "small" (see muh2.d). If the problem to be approximated
c requires a grid neither mud2 por muh2 can exactly fit then another option
c is to generate an approximation on a "close grid" using mud2 or muh2.
c Then transfer the result to the required grid using cubic interpolation
c via the package "regridpack"(contact John Adams about this software)
c
c *** note
c
c let G be the nx by ny fine grid on which the approximation is
c generated and let n = max0(iex,jey). in mudpack, multigrid
c cycling is implemented on the ascending chain of grids
c
c G(1) < ... < G(k) < ... < G(n) = G.
c
c each G(k) (k=1,...,n) has mx(k) by my(k) grid points
c given by:
c
c mx(k) = ixp*[2**(max0(iex+k-n,1)-1)] + 1
c
c my(k) = jyq*[2**(max0(jey+k-n,1)-1)] + 1
c
c
c
c ... iguess=iparm(12)
c
c = 0 if no initial guess to the pde is provided
c
c = 1 if an initial guess to the pde is at the finest grid
c level is provided in phi (see below)
c
c comments on iguess = 0 or 1 . . .
c
c even if iguess = 0, phi must be initialized at all grid points (this
c is not checked). phi can be set to 0.0 at non-dirchlet grid points
c if nothing better is available. the values set in phi when iguess = 0
c are passed down and serve as an initial guess to the pde at the coarsest
c grid level where cycling commences. in this sense, values input in
c phi always serve as an initial guess. setting iguess = 0 forces full
c multigrid cycling beginning at the coarsest and finishing at the finest
c grid level.
c
c if iguess = 1 then the values input in phi are an initial guess to the
c pde at the finest grid level where cycling begins. this option should
c be used only if a "very good" initial guess is available (as, for
c example, when restarting from a previous iguess=0 call).
c
c time dependent problems . . .
c
c *** assume we are solving an elliptic pde every time step in a
c marching problem of the form:
c
c l(p(t)) = r(t)
c
c where the differential operator "l" has no time dependence,
c "p(t)" is the solution and "r(t)" is the right hand side at
c current time "t". let "dt" be the increment between time steps.
c then p(t) can be used as an initial guess to p(t+dt) with
c intl = 1 when solving
c
c l(p(t+dt)) = r(t+dt).
c
c after the first two time steps, rather than continue, it would
c be better to define the "correction" term:
c
c e(t,dt) = p(t+dt) - p(t)
c
c this clearly satisfies the equation
c
c l(e(t,dt)) = r(t+dt) - r(t).
c
c this should be solved with iguess = 0 and intl = 1. boundary
c conditions for e(t,dt) are obtained from the boundary conditions
c for p(t) by subtracting given values at t from given values at
c t+dt. for example if
c
c d(p(t))/dx = f(t), d(p(t+dt))/dx = f(t+dt)
c
c at some x boundary then e(t,dt) satisfies the derivative
c boundary condition
c
c d(e(t,dt))/dx = f(t+dt) - f(t).
c
c e(t,dt) can be preset to 0.0 (at nondirchlet points) or (if p(t-dt)
c is saved) to p(t)-p(t-dt). with iguess = 0, these values will serve
c as an initial guess to e(t,dt) at the coarsest grid level. this
c approach has the advantage that a full sequence of multigrid cycles,
c beginning at the coarsest grid level, is invoked every time step in
c solving for e(t,dt). a few digits of accuracy in e(t,dt), which is
c ordinarily much smaller than p(t), will yield several more digits of
c accuracy in the final approximation:
c
c p(t+dt) = p(t) + e(t,dt).
c
c using this approach to integrate in time will give more accuracy
c then using p(t) as an initial guess to p(t+dt) for all time steps.
c it does require additional storage.
c
c if the differential operator "l" has time dependence (either thru
c the coefficients in the pde or the coefficients in the derivative
c boundary conditions) then use p(t) as an initial guess to p(t+dt)
c when solving
c
c l(t+dt)(p(t+dt)) = r(t+dt)
c
c with intl = 0 for all time steps (the discretization must be repeated
c for each new "t"). either iguess = 0 (p(t) will then be an initial
c guess at the coarsest grid level where cycles will commence) or
c iguess = 1 (p(t) will then be an initial guess at the finest grid
c level where cycles will remain fixed) can be tried.
c
c
c ... maxcy = iparm(13)
c
c the exact number of cycles executed between the finest (nx by
c ny) and the coarsest ((ixp+1) by (jyq+1)) grid levels when
c tolmax=fparm(5)=0.0 (no error control). when tolmax > 0.0
c is input (error control) then maxcy is a limit on the number
c of cycles between the finest and coarsest grid levels. in
c any case, at most maxcy*(iprer+ipost) relaxation sweeps are
c are performed at the finest grid level (see iprer=mgopt(2),
c ipost=mgopt(3) below). when multigrid iteration is working
c "correctly" only a few are required for convergence. large
c values for maxcy should not be necessary.
c
c
c ... method = iparm(14) determines the method of relaxation
c (gauss-seidel based on alternating points or lines)
c
c = 0 for point relaxation
c
c = 1 for line relaxation in the x direction
c
c = 2 for line relaxation in the y direction
c
c = 3 for line relaxation in both the x and y direction
c
c
c *** choice of method. . .
c
c let fx represent the quantity cxx(x,y)/dlx**2 over the solution region.
c
c let fy represent the quantity cyy(x,y)/dly**2 over the solution region
c
c if fx,fy are roughly the same size and do not vary too much over
c the solution region choose method = 0. if this fails try method=3.
c
c if fx is much greater than fy choose method = 1.
c
c if fy is much greater than fx choose method = 2
c
c if neither fx or fy dominates over the solution region and they
c both vary considerably choose method = 3.
c
c
c ... length = iparm(15)
c
c the length of the work space provided in vector work (see below).
c let isx = 0 if method = 0 or method = 2
c let isx = 3 if method = 1 or method = 3 and nxa.ne.0
c let isx = 5 if method = 1 or method = 3 and nxa.eq.0
c let jsy = 0 if method = 0 or method = 1
c let jsy = 3 if method = 2 or method = 3 and nyc.ne.0
c let jsy = 5 if method = 2 or method = 3 and nyc.eq.0
c then . . .
c
c length = 4*[nx*ny*(10+isx+jsy)+8*(nx+ny+2)]/3
c
c will suffice in most cases. the exact minimal work space
c length required for the current nx,ny and method is output
c in iparm(16) (even if iparm(15) is too small). this will be
c less then the value given by the simplified formula above
c in most cases.
c
c
c ... fparm
c
c a floating point vector of length 6 used to efficiently
c pass floating point arguments. fparm is set internally
c in mud2 and defined as follows . . .
c
c
c ... xa=fparm(1), xb=fparm(2)
c
c the range of the x independent variable. xa must
c be less than xb
c
c
c ... yc=fparm(3), yd=fparm(4)
c
c the range of the y independent variable. yc must
c be less than yd.
c
c
c ... tolmax = fparm(5)
c
c when input positive, tolmax is a maximum relative error tolerance
c used to terminate the relaxation iterations. assume phi1(i,j)
c and phi2(i,j) are the last two computed approximations at all
c grid points of the finest grid level. if we define
c
c phdif = max(abs(phi2(i,j)-phi1(i,j))) for all i,j
c
c and
c
c phmax = max(abs(phi2(i,j))) for all i,j
c
c then "convergence" is considered to have occurred if and only if
c
c phdif/phmax < tolmax.
c
c
c if tolmax=fparm(5)=0.0 is input then there is no error control
c and maxcy cycles from the finest grid level are executed. maxcy
c is a limit which cannot be exceeded even with error control.
c *** calls with tolmax=0.0, when appropriate because of known
c convergence behavior, are more efficient than calls with tolmax
c positive (i.e., if possible DO NOT use error control!).
c
c ... work
c
c a one dimensional real saved work space (see iparm(15) for
c length) which must be preserved from the previous call when
c calling with intl=iparm(1)=1.
c
c ... bndyc
c
c a subroutine with arguments (kbdy,xory,alfa,gbdy) which
c are used to input mixed boundary conditions to mud2. bndyc
c must be declared "external" in the program calling mud2.
c the boundaries are numbered one thru four and the mixed
c derivative boundary conditions are described below (see the
c sample driver code "tmud2.f" for an example of how bndyc is
c can beset up).
c
c * * * * * * * * * * * * y=yd
c * kbdy=4 *
c * *
c * *
c * *
c * kbdy=1 kbdy=2 *
c * *
c * *
c * *
c * kbdy=3 *
c * * * * * * * * * * * * y=yc
c
c x=xa x=xb
c
c
c (1) the kbdy=1 boundary
c
c this is the edge x=xa where nxa=iparm(2)=2 flags
c a mixed boundary condition of the form
c
c dp/dx + alfxa(y)*p(xa,y) = gbdxa(y)
c
c in this case kbdy=1,xory=y will be input to bndyc and
c alfa,gbdy corresponding to alfxa(y),gbdxa(y) must be returned.
c
c
c (2) the kbdy=2 boundary
c
c this is the edge x=xb where nxb=iparm(3)=2 flags
c a mixed boundary condition of the form
c
c dp/dx + alfxb(y)*p(xb,y) = gbdxb(y)
c
c in this case kbdy=2,xory=y, will be input to bndyc and
c alfa,gbdy corresponding to alfxb(y),gbdxb(y) must be returned.
c
c
c (3) the kbdy=3 boundary
c
c this is the edge y=yc where nyc=iparm(4)=2 flags
c a mixed boundary condition of the form
c
c dp/dy + alfyc(x)*p(x,yc) = gbdyc(x)
c
c in this case kbdy=3,xory=x will be input to bndyc and
c alfa,gbdy corresponding to alfyc(x),gbdyc(x) must be returned.
c
c
c (4) the kbdy=4 boundary
c
c this is the edge y=yd where nyd=iparm(5)=2 flags
c a mixed boundary condition of the form
c
c dp/dy + alfyd(x)*p(x,yd) = gbdyd(x)
c
c in this case kbdy=4,xory=x will be input to bndyc and
c alfa,gbdy corresponding to alfyd(x),gbdyd(x) must be returned.
c
c
c *** bndyc must provide the mixed boundary condition values
c in correspondence with those flagged in iparm(2) thru
c iparm(5). if all boundaries are specified or periodic
c mud2 will never call bndyc. even then it must be entered
c as a dummy subroutine. bndyc must be declared "external"
c in the routine calling mud2. the actual name chosen may
c be different.
c
c
c ... coef
c
c a subroutine with arguments (x,y,cxx,cyy,cx,cy,ce) which
c provides the known real coefficients for the elliptic pde at
c any grid point (x,y). the name chosen in the calling routine
c may be different where the coefficient routine must be declared
c "external."
c
c ... rhs
c
c an array dimensioned nx by ny which contains the given
c right hand side values on the uniform 2-d mesh.
c
c rhs(i,j) = r(xi,yj) for i=1,...,nx and j=1,...,ny
c
c ... phi
c
c an array dimensioned nx by ny. on input phi must contain
c specified boundary values. for example, if nyd=iparm(5)=1
c then phi(i,ny) must be set equal to p(xi,yd) for i=1,...nx
c prior to calling mud2. these values are preserved by mud2.
c if an initial guess is provided (iguess=iparm(11)=1) it must
c be input thru phi.
c
c
c *** if no initial guess is given (iguess=0) then phi must still
c be initialized at all grid points (this is not checked). these
c values will serve as an initial guess to the pde at the coarsest
c grid level after a transfer from the fine solution grid. set phi
c equal to to 0.0 at all internal and non-specified boundaries
c grid points if nothing better is available.
c
c
c ... mgopt
c
c an integer vector of length 4 which allows the user to select
c among various multigrid options. if mgopt(1)=0 is input then
c a default set of multigrid arguments (chosen for robustness)
c will be internally selected and the remaining values in mgopt
c will be ignored. if mgopt(1) is nonzero then the arguments
c in mgopt are set internally and defined as follows: (see the
c basic coarse grid correction algorithm below)
c
c
c kcycle = mgopt(1)
c
c = 0 if default multigrid options are to be used
c
c = 1 if v cycling is to be used (the least expensive per cycle)
c
c = 2 if w cycling is to be used (the default)
c
c > 2 if more general k cycling is to be used
c *** warning--values larger than 2 increase
c the execution time per cycle considerably and
c result in the nonfatal error ierror = -5
c which indicates inefficient multigrid cycling.
c
c iprer = mgopt(2)
c
c the number of "pre-relaxation" sweeps executed before the
c residual is restricted and cycling is invoked at the next
c coarser grid level (default value is 2 whenever mgopt(1)=0)
c
c ipost = mgopt(3)
c
c the number of "post relaxation" sweeps executed after cycling
c has been invoked at the next coarser grid level and the residual
c correction has been transferred back (default value is 1
c whenever mgopt(1)=0).
c
c *** if iprer, ipost, or (especially) kcycle is greater than 2
c than inefficient multigrid cycling has probably been chosen and
c the nonfatal error (see below) ierror = -5 will be set. note
c this warning may be overridden by any other nonzero value
c for ierror.
c
c intpol = mgopt(4)
c
c = 1 if multilinear prolongation (interpolation) is used to
c transfer residual corrections and the pde approximation
c from coarse to fine grids within full multigrid cycling.
c
c = 3 if multicubic prolongation (interpolation) is used to
c transfer residual corrections and the pde approximation
c from coarse to fine grids within full multigrid cycling.
c (this is the default value whenever mgopt(1)=0).
c
c *** the default values (2,2,1,3) in the vector mgopt were chosen for
c robustness. in some cases v(2,1) cycles with linear prolongation will
c give good results with less computation (especially in two-dimensions).
c this was the default and only choice in an earlier version of mudpack
c (see [1]) and can be set with the integer vector (1,2,1,1) in mgopt.
c
c *** the schedules for one full multigrid cycle (iguess=0) using v(2,1)
c cycles and w(2,1) cycles are depicted for a four level grid below.
c the number of relaxation sweeps when each grid is visited are indicated.
c the "*" stands for prolongation of the full approximation and the "."
c stands for transfer of residuals and residual corrections within the
c coarse grid correction algorithm (see below). all version 5.0.1
c mudpack solvers use only fully weighted residual restriction
c
c one fmg with v(2,1) cycles:
c
c
c ------------------------------2-----------------1------ level 4
c * . .
c * . .
c ---------------2-----------1-----2-----------1--------- level 3
c * . . . .
c * . . . .
c ------2-----1-----2-----1-----------2-----1------------ level 2
c * . . . . . .
c * . . . . . .
c ---3-----3-----------3-----------------3--------------- level 1
c
c
c one fmg with w(2,1) cycles:
c
c ------------------------2---------------------------1-- level 4
c * . .
c ----------2-----------1---2-----------3-----------1---- level 3
c * . . . . . .
c ----2---1---2---3---1-------2---3---1---2---3---1------ level 2
c * . . . . . . . . . . . . . .
c --6---6-------6---6-----------6---6-------6---6-------- level 1
c
c
c the form of the "recursive" coarse grid correction cycling used
c when kcycle.ge.0 is input is described below in pseudo-algorithmic
c language. it is implemented non-recursively in fortran in mudpack.
c
c algorithm cgc(k,l(k),u(k),r(k),kcycle,iprer,ipost,iresw,intpol)
c
c *** approximately solve l(k)*u(k) = r(k) using multigrid iteration
c *** k is the current grid level
c *** l(k) is the discretized pde operator at level k
c *** u(k) is the initial guess at level k
c *** r(k) is the right hand side at level k
c *** i(k,k-1) is the restriction operator from level k to level k-1
c *** (the form of i(k,k-1) depends on iresw)
c *** i(k-1,k) is the prolongation operator from level k-1 to level k
c *** (the form of i(k-1,k) depends on intpol)
c
c begin algorithm cgc
c
c *** pre-relax at level k
c
c . do (i=1,iprer)
c
c . . relax(l(k),u(k),r(k))
c
c . end do
c
c . if (k > 1) then
c
c *** restrict the residual from level k to level k-1
c
c . . r(k-1) = i(k,k-1)(r(k)-l(k)*u(k))
c
c . . kount = 0
c
c . . repeat
c
c *** solve for the residual correction at level k-1 in u(k-1)
c *** using algorithm cgc "kcycle" times (this is the recursion)
c
c . . . kount = kount+1
c
c . . . invoke cgc(k-1,l(k-1),u(k-1),r(k-1),kcycle,iprer,ipost,iresw)
c
c
c . . until (kount.eq.kcycle)
c
c *** transfer residual correction in u(k-1) to level k
c *** with the prolongation operator and add to u(k)
c
c . . u(k) = u(k) + i(k-1,k)(u(k-1))
c
c . end if
c
c *** post relax at level k
c
c . do (i=1,ipost)
c
c . . relax(l(k),u(k),r(k))
c
c . end do
c
c . return
c
c end algorithm cgc
c
c
c **********************************************************************
c *** output arguments ************************************************
c **********************************************************************
c
c
c ... iparm(16) *** set for intl=0 calls only
c
c on output iparm(16) contains the actual work space length
c required. this will usually be less than that given by the
c simplified formula for length=iparm(15) (see as input argument)
c
c
c ... iparm(17) *** set for intl=1 calls only
c
c on output iparm(17) contains the actual number of multigrid cycles
c between the finest and coarsest grid levels used to obtain the
c approximation when error control (tolmax > 0.0) is set.
c
c
c ... fparm(6) *** set for intl=1 calls with fparm(5) > 0. only
c
c on output fparm(6) contains the final computed maximum relative
c difference between the last two iterates at the finest grid level.
c fparm(6) is computed only if there is error control (tolmax > 0.0)
c assume phi1(i,j,k) and phi2(i,j,k) are the last two computed
c values for phi(i,j,k) at all points of the finest grid level.
c if we define
c
c phdif = max(abs(phi2(i,j)-phi1(i,j))) over all i,j
c
c and
c
c phmax = max(abs(phi2(i,j)) over all i,j
c
c then
c
c fparm(6) = phdif/phmax
c
c is returned whenever phmax > 0.0. in the degenerate case
c phmax = 0.0, fparm(6) = phdif is returned.
c
c
c ... work
c
c on output work contains intermediate values that must not
c be destroyed if mud2 is to be called again with intl=1
c
c
c ... phi *** for intl=1 calls only
c
c on output phi(i,j) contains the approximation to p(xi,yj)
c for all mesh points i = 1,...,nx and j=1,...,ny. the last
c computed iterate in phi is returned even if convergence is
c not obtained
c
c
c ... ierror
c
c For intl=iparm(1)=0 initialization calls, ierror is an
c error flag that indicates invalid input arguments when
c returned positive and nonfatal warnings when returned
c negative. Argument checking and discretization
c is bypassed for intl=1 calls which can only return
c ierror = -1 or 0 or 1.
c
c
c non-fatal warnings * * *
c
c
c =-5 if kcycle=mgopt(1) is greater than 2. values larger than 2 results
c in an algorithm which probably does far more computation than
c necessary. kcycle = 1 (v cycles) or kcycle=2 (w cycles) should
c suffice for most problems. ierror = -5 is also set if either
c iprer = mgopt(2) or ipost=mgopt(3) is greater than 2. the
c ierror=-5 flag is overridden by any other fatal or non-fatal
c error.
c
c =-4 if there are dominant nonzero first order terms in the pde which
c make it "hyperbolic" at the finest grid level. numerically, this
c happens if:
c
c abs(cx)*dlx > 2.*abs(cxx) (dlx = (xb-xa)/(nx-1))
c
c (or)
c
c abs(cy)*dly > 2.*abs(cyy) (dly = (yd-yc)/(ny-1))
c
c
c at some fine grid point (xi,yj). if an adjustment is not made the
c condition can lead to a matrix coming from the discretization
c which is not diagonally dominant and divergence is possible. since
c the condition is "likely" at coarser grid levels for pde's with
c nonzero first order terms, the adjustments (actually first order
c approximations to the pde)
c
c
c cxx = amax1(cxx,0.5*abs(cx)*dx)
c
c (and)
c
c cyy = amax1(cyy,0.5*abs(cy)*dy)
c
c
c (here dx,dy are the x,y mesh sizes of the subgrid)
c
c are made to preserve convergence of multigrid iteration. if made
c at the finest grid level, it can lead to convergence to an
c erroneous solution (flagged by ierror = -4). a possible remedy
c is to increase resolution. the ierror = -4 flag overrides the
c nonfatal ierror = -5 flag.
c
c
c =-3 if the continuous elliptic pde is singular. this means the
c boundary conditions are periodic or pure derivative at all
c boundaries and ce(x,y) = 0.0 for all x,y. a solution is still
c attempted but convergence may not occur due to ill-conditioning
c of the linear system coming from the discretization. the
c ierror = -3 flag overrides the ierror=-4 and ierror=-5 nonfatal
c flags.
c
c
c =-2 if the pde is not elliptic (i.e., cxx*cyy.le.0.0 for some (xi,yj))
c in this case a solution is still attempted although convergence
c may not occur due to ill-conditioning of the linear system.
c the ierror = -2 flag overrides the ierror=-5,-4,-3 nonfatal
c flags.
c
c
c =-1 if convergence to the tolerance specified in tolmax=fparm(5)>0.
c is not obtained in maxcy=iparm(13) multigrid cycles between the
c coarsest (ixp+1,jyq+1) and finest (nx,ny) grid levels.
c in this case the last computed iterate is still returned.
c the ierror = -1 flag overrides all other nonfatal flags
c
c
c no errors * * *
c
c = 0
c
c fatal argument errors * * *
c
c = 1 if intl=iparm(1) is not 0 on initial call or not 0 or 1
c on subsequent calls
c
c = 2 if any of the boundary condition flags nxa,nxb,nyc,nyd
c in iparm(2),iparm(3),iparm(4),iparm(5) are not 0,1 or 2
c or if nxa,nxb or nyc,nyd are not pairwise zero.
c
c = 3 if mino(ixp,jyq) < 2 (ixp = iparm(6), jyq = iparm(7))
c
c = 4 if min0(iex,jey) < 1 (iex = iparm(8), jey = iparm(9)) or
c if max0(iex,jey) > 50
c
c = 5 if nx.ne.ixp*2**(iex-1)+1 or ny.ne.jyq*2**(jey-1)+1
c (nx = iparm(10), ny = iparm(11))
c
c = 6 if iguess = iparm(12) is not equal to 0 or 1
c
c = 7 if maxcy = iparm(13) < 1
c
c = 8 if method = iparm(14) is not 0,1,2, or 3
c
c = 9 if length = iparm(15) is too small (see iparm(16) on output
c for minimum required work space length)
c
c =10 if xa >= xb or yc >= yd
c (xa=fparm(1),xb=fparm(2),yc=fparm(3),yd=fparm(4))
c
c =11 if tolmax = fparm(5) < 0.0
c
c errors in setting multigrid options * * * (see also ierror=-5)
c
c =12 if kcycle = mgopt(1) < 0 or
c if iprer = mgopt(2) < 1 or
c if ipost = mgopt(3) < 1 or
c if intpol = mgopt(4) is not 1 or 3
c
c *********************************************************
\end{lstlisting}
%==================================== Bibliographie ==================================
\end{document}
```