SIAMFANLCh3.ipynb

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    "$\\newcommand{\\calf}{{\\cal F}}\n",
    "\\newcommand{\\calk}{{\\cal K}}\n",
    "\\newcommand{\\calp}{{\\cal P}}\n",
    "\\newcommand{\\dnu}{d \\nu}\n",
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    "\\newcommand{\\md}{{\\bf D}}\n",
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    "\\newcommand{\\mU}{{\\bf U}}\n",
    "\\newcommand{\\vu}{{\\bf u}}\n",
    "\\newcommand{\\vx}{{\\bf x}}\n",
    "\\newcommand{\\vw}{{\\bf w}}\n",
    "\\newcommand{\\vy}{{\\bf y}}\n",
    "\\newcommand{\\vf}{{\\bf f}}\n",
    "\\newcommand{\\vs}{{\\bf s}}\n",
    "\\newcommand{\\ve}{{\\bf e}}\n",
    "\\newcommand{\\vd}{{\\bf d}}\n",
    "\\newcommand{\\vb}{{\\bf b}}\n",
    "\\newcommand{\\vg}{{\\bf g}}\n",
    "\\newcommand{\\vz}{{\\bf z}}\n",
    "\\newcommand{\\vr}{{\\bf r}}\n",
    "\\newcommand{\\mg}{{\\bf G}}\n",
    "\\newcommand{\\ml}{{\\bf L}}\n",
    "\\newcommand{\\mg}{{\\bf G}}\n",
    "\\newcommand{\\mv}{{\\bf V}}\n",
    "\\newcommand{\\ma}{{\\bf A}}\n",
    "\\newcommand{\\mi}{{\\bf I}}\n",
    "\\newcommand{\\mm}{{\\bf M}}\n",
    "\\newcommand{\\mb}{{\\bf B}}\n",
    "\\newcommand{\\ball}{{\\cal B}}\n",
    "\\newcommand{\\ptc}{{\\Psi TC}}\n",
    "\\newcommand{\\diag}{\\mbox{diag}}\n",
    "\\newcommand{\\begeq}{{\\begin{equation}}}\n",
    "\\newcommand{\\endeq}{{\\end{equation}}}\n",
    "$"
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    "## Files for this Chapter\n",
    "\n",
    "- From the Package repository:\n",
    "  - Solvers for Scalar Equations: __/src/Solvers__\n",
    "    - Newton's method: __nsoli.jl__\n",
    "    - Pseudo-Transiant continuation: __ptcsoli.jl__\n",
    "  - Krylov linear solvers\n",
    "    - GMRES: __kl_gmres.jl__\n",
    "    - BiCGSTAB: __kl_bicgstab.jl__\n",
    "  - Test Problems: __/src/TestProblems/Systems__\n",
    "    - H-equation: __Hequation.jl__\n",
    "    - Convection-Diffusion equation: __EllipticPDE.jl__ and __PDE\\_Tools.jl__\n",
    "    - Buckling Beam: __FBeam!.jl__\n",
    "    - Two-point boundary value problem: __Fbvp!.jl__\n",
    "\n",
    "- From the Notebook repository: __/src/Chapter3__\n",
    "   - Julia codes that generate the plots and the tables"
   ]
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   "metadata": {},
   "source": [
    "# Chapter 3: Newton-Krylov Methods"
   ]
  },
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   "source": [
    "## Contents for Chapter 3\n",
    "\n",
    "[Section 3.1: Newton Iterative Methods](#Section-3.1:-Newton-Iterative-Methods)\n",
    "\n",
    "[Section 3.2: Krylov Methods for Solving Linear Equations](#Section-3.2:-Krylov-Methods-for-Solving-Linear-Equations)\n",
    "\n",
    "[Section 3.3: Preconditioning](#Section-3.3:-Preconditioning)\n",
    "\n",
    "[Section 3.4: Computing an Approximate Newton Step](#Section-3.4:-Computing-an-Approximate-Newton-Step)\n",
    "\n",
    "[Section 3.5: Pseudo-Transient Continuation](#Section-3.5:-Pseudo-Transient-Continuation)\n",
    "\n",
    "[Section 3.6: What Can Go Wrong?](#Section-3.6:-What-Can-Go-Wrong?)\n",
    "\n",
    "[Section 3.7: Newton-Krylov Examples](#Section-3.7:-Newton-Krylov-Examples)"
   ]
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    "## Section 3.1: Newton Iterative Methods\n",
    "\n",
    "Recall from section __1.4__ that an inexact Newton method\n",
    "approximates the Newton direction with a vector $\\vd$ satisfying the __inexact Newton condition__. This means that\n",
    "$$\n",
    "\\| \\mf'(\\vx_n) \\vd + \\mf(\\vx_n) \\| \\le \\eta \\| \\mf(\\vx_n) \\|.\n",
    "$$\n",
    " The parameter $\\eta$ is called the\n",
    "__forcing term__. \n",
    "\n",
    "Newton iterative methods realize the inexact Newton condition\n",
    "by applying a linear iterative method to the\n",
    "equation for the Newton step and terminating that iteration\n",
    "when the inexact Newton condition holds. We sometimes refer to this linear iteration\n",
    "as an __inner iteration__. Similarly,\n",
    "the nonlinear iteration (the while loop in Algorithm __nsol__)\n",
    "is often called the __outer iteration__.\n",
    "\n",
    "The Newton-Krylov methods, as the name suggests, use Krylov\n",
    "subspace-based linear solvers. The methods differ in storage\n",
    "requirements, cost in evaluations of $\\mf$, and robustness.\n",
    "Our solver package __SIAMFANLEquations.jl__ has two\n",
    "Newton-Krylov nonlinear solvers, __src/Solvers/nsoli.jl__ and __src/Solvers/ptcsoli.jl__.\n",
    "We include two Krylov linear solvers,\n",
    "GMRES \n",
    "<cite data-cite=\"gmres\"><a href=\"siamfa.html#gmres\">(SS86)</cite>\n",
    "in the function __src/Solvers/LinearSolvers/kl\\_gmres.jl__ and\n",
    "and BiCGSTAB \n",
    "<cite data-cite=\"bicgstab\"><a href=\"siamfa.html#bicgstab\">(vdV92)</cite>\n",
    "in the function __src/Solvers/LinearSolvers/kl\\_bicgstab.jl__. Our linear solvers have been designed\n",
    "to work well with nonlinear solvers. For example, information from the\n",
    "nonlinear solver can be used to adjust the forcing term in the linear\n",
    "solver. Following convention,\n",
    "we will refer to the nonlinear methods as\n",
    "Newton-GMRES and Newton-BiCGSTAB."
   ]
  },
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   "source": [
    "## Section 3.2: Krylov Methods for Solving Linear Equations"
   ]
  },
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   "source": [
    "Krylov iterative\n",
    "methods approximate the solution of a linear system $\\ma \\vx = \\vb$\n",
    "with a sum of the form\n",
    "$$\n",
    "\\vx_k = \\vx_0 + \\sum_{j=0}^{k-1} \\gamma_k \\ma^k \\vr_0,\n",
    "$$\n",
    "where $\\vr_0 = \\vb - \\ma \\vx_0$ and $\\vx_0$ is the initial iterate.\n",
    "If the goal is to approximate a Newton step, as it is here,\n",
    "the most sensible initial iterate is $\\vx_0 = 0$, because we\n",
    "have no _a priori_ knowledge of the direction, but, at least\n",
    "in the local phase of the iteration, expect the norm of the step to be small.\n",
    "\n",
    "We express this in compact form as $\\vx_k \\in {\\cal K}_k$,\n",
    "where the $k$th __Krylov subspace__ is\n",
    "$$\n",
    "{\\cal K}_k = \\mbox{span}(\\vr_0, \\ma \\vr_0, \\dots, \\ma^{k-1} \\vr_0 ).\n",
    "$$\n",
    "Krylov methods build the iteration by evaluating matrix-vector\n",
    "products, in very different ways, to build an iterate in the\n",
    "appropriate Krylov subspace. Our nonlinear solvers, like most\n",
    "implementations of Newton-Krylov methods,\n",
    "approximate Jacobian-vector products with forward\n",
    "differences as the default. You have\n",
    "the option of providing an analytic Jacobian-vector project. Unlike\n",
    "the case for direct linear solvers, there is rarely any gain inK\n",
    "performance or robustness in using an analytic Jacobian-vector product.\n",
    "If you find that the iteration is stagnating, your first step should\n",
    "be to reduce the forcing term. After that you\n",
    "might see if an analytic Jacobian-vector product helps, but it is not\n",
    "likely that it will.\n"
   ]
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    "### 3.2.1 GMRES\n",
    "\n",
    "The easiest Krylov method to understand is the GMRES\n",
    "<cite data-cite=\"gmres\"><a href=\"siamfa.html#gmres\">(SS86)</cite>\n",
    "method, the default linear solver in __nsoli.jl__. The\n",
    "$k$th GMRES iterate is the solution of the linear least squares\n",
    "problem of minimizing\n",
    "$$\n",
    "\\| \\vb - \\ma \\vx_k \\|^2\n",
    "$$\n",
    "over ${\\cal K}_k$.\n",
    "We refer the reader to \n",
    "<cite data-cite=\"gmres\"><a href=\"siamfa.html#gmres\">(SS86)</cite>,\n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>,\n",
    "<cite data-cite=\"trefbau\"><a href=\"siamfa.html#trefbau\">(TB96)</cite>,\n",
    "<cite data-cite=\"demmel\"><a href=\"siamfa.html#demmel\">(Dem97)</cite>,\n",
    "for\n",
    "the details of the implementation, pointing out only that it is\n",
    "not a trivial task to implement GMRES well.\n",
    "\n",
    "GMRES must accumulate the history of the linear iteration as an\n",
    "orthonormal basis for the Krylov subspace. This is an important\n",
    "property of the method because one can, and often does for large\n",
    "problems, exhaust the available fast memory. Any implementation of\n",
    "GMRES must limit the size of the Krylov subspace. GMRES($m$)\n",
    "does this by restarting the iteration when the\n",
    "size of the Krylov space exceeds $m$ vectors. With __nsoli.jl__\n",
    "you must allocate storage of $m+1$ vectors for the Krylov basis for\n",
    "GMRES($m$). The if you ask for more than $m$ GMRES iterations the iteration\n",
    "will restart. The default is to take $m$ GMRES iterations or fewer before\n",
    "termination. The convergence theory for GMRES does not\n",
    "apply to GMRES($m$) and the performance of GMRES($m$) can be poor if\n",
    "$m$ is small.\n",
    "\n",
    "GMRES, like other Krylov methods,\n",
    "is often, but not always, implemented as a\n",
    "__matrix-free__ method. The reason for this\n",
    "is that only matrix-vector products, rather than details of the\n",
    "matrix itself, are needed to implement a Krylov method. One example\n",
    "of a matrix-free method is the use of a forward difference Jacobian-vector product.\n",
    "\n",
    "#### Convergence of GMRES\n",
    "    \n",
    "As a general rule (but not an absolute law!\n",
    "<cite data-cite=\"trefa\"><a href=\"siamfa.html#trefa\">(NRT92)</cite>),\n",
    "GMRES, like other Krylov methods, performs best if the eigenvalues of\n",
    "$\\ma$ are in a few tight clusters \n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>,\n",
    "<cite data-cite=\"ctk:jordan\"><a href=\"siamfa.html#ctk:jordan\">(CIKM96)</cite>,\n",
    "<cite data-cite=\"trefbau\"><a href=\"siamfa.html#trefbau\">(TB96)</cite>,\n",
    "<cite data-cite=\"demmel\"><a href=\"siamfa.html#demmel\">(Dem97)</cite>.\n",
    "\n",
    "One way to understand this, keeping in mind that $\\vx_0 = 0$, is\n",
    "to observe that the $k$th GMRES residual is in ${\\cal K}_k$ and hence\n",
    "can be written as a polynomial in $\\ma$ applied to the residual\n",
    "$$\n",
    "\\vr_k = \\vb - \\ma \\vx_k = p(\\ma) \\vr_0 = p(\\ma) \\vb.\n",
    "$$\n",
    "Here $p \\in {\\cal P}_k$, the set of $k$th-degree\n",
    "__residual polynomials__. This is the\n",
    "set of polynomials of degree $k$ with $p(0) = 1$. Since the $k$th\n",
    "GMRES iteration satisfies\n",
    "$$\n",
    "\\| \\vb - \\ma \\vx_k \\| \\le \\| \\vb - \\ma \\vz \\|\n",
    "$$\n",
    "for all $\\vz \\in \\calk_k$, we must have \n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>\n",
    "$$\n",
    "\\| \\vr_k \\| = \\min_{p \\in \\calp_k} \\| p(\\ma) \\vr_0 \\|.\n",
    "$$\n",
    "This simple fact can lead to very useful error estimates.\n",
    "\n",
    "Here, for example, is a convergence result for diagonalizable matrices.\n",
    "$\\ma$ is __diagonalizable__\n",
    "if there is a nonsingular matrix $\\mv$ such that\n",
    "$$\n",
    "\\ma = \\mv \\Lambda \\mv^{-1}.\n",
    "$$\n",
    "Here $\\Lambda$ is a\n",
    "diagonal matrix with the eigenvalues of $\\ma$ on the\n",
    "diagonal. If $\\ma$ is a diagonalizable matrix\n",
    "and $p$ is a polynomial, then\n",
    "$$\n",
    "p(\\ma) = \\mv p(\\Lambda) \\mv^{-1}.\n",
    "$$\n",
    "$\\ma$ is __normal__\n",
    "if\n",
    "the __diagonalizing transformation__\n",
    "$\\mv$ is __unitary__.\n",
    "In this case the columns of $\\mv$ are\n",
    "the eigenvectors of $\\ma$ and $\\mv^{-1} = \\mv^H$. Here $\\mv^H$ is the complex\n",
    "conjugate transpose of $\\mv$.\n",
    "The reader should be aware that $\\mv$ and $\\Lambda$ can be complex even\n",
    "if $\\ma$ is real.\n",
    "\n",
    "___\n",
    "__Theorem 3.1.__ \n",
    "Let $\\ma = \\mv \\Lambda \\mv^{-1}$ be a nonsingular diagonalizable matrix.\n",
    "Let $\\vx_k$ be the $k$th GMRES iterate. Then, for all\n",
    "$\\bar p_k \\in \\calp_k$,\n",
    "$$\n",
    "\\frac{\\| \\vr_k \\|}{\\| \\vr_0 \\|} \\le \\kappa_2(\\mv)\n",
    "\\max_{z \\in \\sigma(\\ma)} | {\\bar p}_k(z) |,\n",
    "$$\n",
    "where $\\sigma(\\ma)$ is the set of eigenvalues of $\\ma$.\n",
    "___\n",
    "    \n",
    "    \n",
    "___\n",
    "__Proof:__\n",
    "Let $\\bar p_k \\in \\calp_k$.\n",
    "We can easily estimate $\\| \\bar p_k(\\ma) \\|$ by\n",
    "$$\n",
    "\\| \\bar p_k(\\ma) \\| \\le \\| \\mf \\| \\| \\mv^{-1} \\| \\| \\bar p_k(\\Lambda) \\|\n",
    "\\le \\kappa_2(\\mv) \\max_{z \\in \\sigma(\\ma)} | \\bar p_k(z) |,\n",
    "$$\n",
    "as asserted\n",
    "___\n",
    "\n",
    "    \n",
    "Suppose, for example, that $\\ma$ is diagonalizable,\n",
    "$\\kappa(\\mv) = 100$, and all the eigenvalues of $A$ lie in a\n",
    "disk of radius $0.1$ centered about $1$ in the complex plane.\n",
    "The theorem above implies (using ${\\bar p}_k(z) = (1 - z)^k$) that\n",
    "$$\n",
    "\\frac{\\| \\vr_k \\|}{\\| \\vr_0 \\|}\\le 100 (0.1)^k = 0.1^{k-2}.\n",
    "$$\n",
    "Hence, GMRES will reduce the residual by a factor of, say, $10^5$ after\n",
    "seven iterations. Since reduction of the residual is the goal of the\n",
    "linear iteration in an inexact Newton method, this is a very useful\n",
    "bound. See \n",
    "<cite data-cite=\"ctk:jordan\"><a href=\"siamfa.html#ctk:jordan\">(CIKM96)</cite>\n",
    "    for examples of similar estimates when\n",
    "the eigenvalues are contained in a small number of clusters.\n",
    "One objective of  preconditioning (see section 3.2)\n",
    "is to change $\\ma$ to obtain an advantageous\n",
    "distribution of eigenvalues.\n"
   ]
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   "source": [
    "### Section 3.2.2: Low Storage Krylov Methods"
   ]
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    "If $\\ma$ is symmetric and positive definite, the conjugate gradient\n",
    "(CG)\n",
    "\\index{Conjugate gradient method}\n",
    "method \\cite{cg} has better convergence and storage properties\n",
    "than the more generally applicable Krylov methods. In exact arithmetic\n",
    "the $k$th CG iteration minimizes\n",
    "$$\n",
    "(\\vx - \\vx^*)^T \\ma (\\vx - \\vx^*) = \\ve^T \\ma \\ve = \\| \\ve \\|_A^2\n",
    "$$\n",
    "over the $k$th Krylov subspace. The symmetry and positivity\n",
    "can be exploited so that the storage requirements do not grow with the\n",
    "number of iterations.\n",
    "\n",
    "A tempting idea is to multiply a general system $\\ma \\vx = \\vb$\n",
    "by $\\ma^T$ to obtain the\n",
    "__normal equations__ $\\ma^T \\ma \\vx = \\ma^T \\vb$ and then apply CG to\n",
    "the new problem, which has a symmetric positive definite\n",
    "coefficient matrix $\\ma^T \\ma$. This\n",
    "approach, called CGNR, has\n",
    "the disadvantage that the condition number of $\\ma^T \\ma$ is the square\n",
    "of that of $\\ma$, and hence the convergence of the CG iteration can\n",
    "be far too slow. A similar approach,\n",
    "called CGNE, solves $\\ma \\ma^T \\vz = \\vb$\n",
    "with CG and then sets $\\vx = \\ma^T \\vz$. Because the condition number is\n",
    "squared and a transpose-vector multiplication is needed, CGNR and CGNE\n",
    "are used far less frequently than the other low-storage methods. \n",
    "\n",
    "The need for a transpose-vector multiplication is a major problem\n",
    "unless one wants to store the Jacobian matrix. It is simple, as we have seen,\n",
    "to approximate a Jacobian-vector\n",
    "product with a forward difference, but no matrix-free way to\n",
    "obtain a transpose-vector product is known.\n",
    "\n",
    "Low-storage alternatives to GMRES that do not need a transpose-vector\n",
    "product are available \n",
    "<cite data-cite=\"bicgstab\"><a href=\"siamfa.html#bicgstab\">(vdV92)</cite>,\n",
    "<cite data-cite=\"tfqmr\"><a href=\"siamfa.html#tfqmr\">(Fre93)</cite>,\n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>,\n",
    "but do not\n",
    "have the robust theoretical properties of GMRES or CG.\n",
    "Aside from GMRES($m$), one such low-storage solver, BiCGSTAB \n",
    "<cite data-cite=\"bicgstab\"><a href=\"siamfa.html#bicgstab\">(vdV92)</cite> \n",
    "can be used in __nsoli.jl__.\n",
    "\n",
    "We refer the reader to \n",
    "<cite data-cite=\"bicgstab\"><a href=\"siamfa.html#bicgstab\">(vdV92)</cite>,\n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>, and\n",
    "<cite data-cite=\"anne\"><a href=\"siamfa.html#anne\">(Gre97)</cite>\n",
    "for detailed descriptions of this and other low-storage methods. If you consider\n",
    "BiCGSTAB and or GMRES(m) as solvers, you should be aware that,\n",
    "while both have the advantage of a fixed storage requirement\n",
    "throughout the linear iteration, there are some problems.\n",
    "\n",
    "BiCGSTAB can __break down__. This means that\n",
    "the iteration will cause a division by zero. This is not an\n",
    "artifact of the floating point number system but is\n",
    "intrinsic to the methods.  While GMRES($m$)\n",
    "can also fail to converge, that failure will manifest itself\n",
    "as a stagnation in the iteration, not a division by zero or\n",
    "an overflow.\n",
    "\n",
    "The number of linear iterations that BiCGSTAB\n",
    "needs for convergence can be roughly the same as for GMRES, but each\n",
    "linear iteration needs two matrix-vector products (i.e., two\n",
    "new evaluations of $\\mf$ f you use forward difference Jacobian-vector\n",
    "products).\n",
    "\n",
    "GMRES($m$) should be your first choice and is built into\n",
    "__kl\\_gmres.jl__ and therefore into __nsoli.jl__ and __ptcsoli.jl__.\n",
    "If, however, you cannot\n",
    "allocate the storage that GMRES($m$) needs to perform well,\n",
    "BiCGSTAB as implemented in __kl\\_bicgstab.jl__, is also accessible from\n",
    "__nsoli.jl__ and  __ptcsoli.jl__ and\n",
    "may solve your problem.\n",
    "\n",
    "If you can store\n",
    "the Jacobian, or can compute a transpose-vector product in an\n",
    "efficient way, and the Jacobian is well conditioned, applying\n",
    "the conjugate gradient\n",
    "(CG) iteration to the normal equations can be\n",
    "a good idea. While the cost of a single iteration is two matrix-vector\n",
    "products, convergence, at least in exact arithmetic,\n",
    "is guaranteed \n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>, and\n",
    "<cite data-cite=\"anne\"><a href=\"siamfa.html#anne\">(Gre97)</cite>.   \n",
    "\n"
   ]
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   "source": [
    "## Section 3.3: Preconditioning"
   ]
  },
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   "source": [
    "__Preconditioning__ the matrix $\\ma$ means\n",
    "multiplying $\\ma$ from the right, left, or both sides by a\n",
    "__reconditioner__ $\\mP$. One does this with the expectation that\n",
    "systems with the coefficient matrix $\\ma \\mP$ or $\\ma \\mP$ are easier to\n",
    "solve than those with $\\ma$. Of course, preconditioning can be done\n",
    "in a matrix-free manner. One needs only a\n",
    "function that performs a preconditioner-vector product.\n",
    "\n",
    "__Left preconditioning__ multiplies the\n",
    "equation $\\ma \\vx = \\vb$ on both sides by $\\mP$ to obtain the\n",
    "__preconditioned system__ $\\mP \\ma \\vx = \\mP\\vb$.\n",
    "One then applies the Krylov\n",
    "method to the preconditioned system. If the condition number of\n",
    "$\\mP \\ma$ is really smaller than that of $\\ma$,\n",
    "the residual of the preconditioned\n",
    "system will be a better reflection of the error than that of the original\n",
    "system. One would hope so, since the preconditioned residual will be used\n",
    "to terminate the linear iteration.\n",
    "\n",
    "__Right preconditioning__ solves the\n",
    "system $\\ma \\mP \\vy = \\vb$ with the Krylov method. Then the solution of the\n",
    "original problem is recovered by setting $\\vx = \\mP \\vy$.\n",
    "Right preconditioning\n",
    "has the feature that the residual upon which termination is based is\n",
    "the residual for the original problem.\n",
    "\n",
    "Two-sided preconditioning replaces $\\ma$ with\n",
    "$\\mP_{left} \\ma \\mP_{right}$.\n",
    "\n",
    "A different approach, which is integrated into some\n",
    "initial value problem codes \n",
    "<cite data-cite=\"brownh86\"><a href=\"siamfa.html#brownh86\">(BH86)</cite>,\n",
    "<cite data-cite=\"brown/daskp\"><a href=\"siamfa.html#brown/daspk\">(BHP94)</cite>, is to\n",
    "pretend that the Jacobian is banded, even if it isn't,\n",
    "and to use Jacobian-vector products and a sparse\n",
    "difference method for banded Jacobians \n",
    "to form a banded approximation to the Jacobian.\n",
    "One factors the banded approximation and uses that\n",
    "factorization as the preconditioner."
   ]
  },
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   "source": [
    "### Section 3.3.1 Preconditioners"
   ]
  },
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   "metadata": {},
   "source": [
    "This section is not an exhaustive account of preconditioning\n",
    "and is only intended to point the reader to the literature.\n",
    "\n",
    "Ideally the preconditioner should be close to the\n",
    "inverse of the Jacobian. In practice, one can get away with\n",
    "far less. If your problem is a discretization of an elliptic\n",
    "differential\n",
    "equation, then a fast solver for the high-order term of the differential\n",
    "operator (with the correct boundary conditions) is an\n",
    "excellent preconditioner \n",
    "<cite data-cite=\"manpart\"><a href=\"siamfa.html#manpart\">(MP90)</cite>.\n",
    "If the high-order term\n",
    "is linear, one might be able to\n",
    "compute the preconditioner-vector product\n",
    "rapidly with, for example, a fast transform method\n",
    " or a multigrid iteration \n",
    "<cite data-cite=\"mgtut2\"><a href=\"siamfa.html#mgtut2\">(BHM00)</cite>.\n",
    "Multigrid methods exploit the smoothing properties of the\n",
    "classical stationary iterative methods by mapping the equation\n",
    "through a sequence of grids. When multigrid methods are used as a solver,\n",
    "one can often show that a solution can be obtained at a cost of\n",
    "$O(N)$ operations, where $N$ is the number of unknowns. Multigrid\n",
    "implementation is difficult and a more typical application is\n",
    "to use a single multigrid iteration (for the high-order term)\n",
    "as a preconditioner.\n",
    "\n",
    "Domain decomposition preconditioners \n",
    "<cite data-cite=\"domain\"><a href=\"siamfa.html#domain\">(SBG96)</cite>,\n",
    "<cite data-cite=\"olofdd\"><a href=\"siamfa.html#olofdd\">(TW05)</cite>,\n",
    "<cite data-cite=\"qvdd\"><a href=\"siamfa.html#qbdd\">(QV99)</cite>,\n",
    "approximate the inverse of the high-order term (or the entire operator)\n",
    "by subdividing the geometric domain of the\n",
    "differential operator, computing the inverses on the subdomains,\n",
    "and combining those inverses. When implemented in an optimal way,\n",
    "the condition number of the preconditioned matrix is independent of\n",
    "the discretization mesh size.\n",
    "\n",
    "Algebraic preconditioners use the sparsity structure of the Jacobian\n",
    "matrix. This is important, for example, for problems that do not\n",
    "come from discretizations of differential or integral equations\n",
    "or for discretizations of differential equations on unstructured\n",
    "grids, which may be generated by computer programs.\n",
    "    \n",
    "An example of such a preconditioner is\n",
    "__algebraic multigrid__ (AMG), which is designed for discretized\n",
    "differential equations on unstructured grids\n",
    "<cite data-cite=\"yangamg\"><a href=\"siamfa.html#yangamg\">(Yan06)</cite>.\n",
    "Algebraic multigrid attempts to recover geometric information from\n",
    "the sparsity pattern of the Jacobian and thereby simulate\n",
    "the intergrid transfers and smoothing used in a conventional\n",
    "geometric multigrid preconditioner. The package\n",
    "__AlgebraicMultigrid.jl__ \n",
    "<cite data-cite=\"amgjl\"><a href=\"siamfa.html#amgjl\">(Ana21)</cite>\n",
    "implements several variations\n",
    "of AMG.\n",
    "\n",
    "Another algebraic\n",
    "approach is \n",
    "__incomplete factorization__\n",
    "<cite data-cite=\"ilu\"><a href=\"siamfa.html#ilu\">(Saa96)</cite>,\n",
    "<cite data-cite=\"saadaxeqb\"><a href=\"siamfa.html#saadaxeqb\">(Saa96b)</cite>. \n",
    "Incomplete factorization preconditioners\n",
    "compute a factorization of a sparse matrix but do not store those elements\n",
    "in the factors that are too small or lie outside a prescribed\n",
    "sparsity pattern. These preconditioners\n",
    "require that the Jacobian be stored as a sparse matrix.\n",
    "The Julia Packages __ILUZero.jl__ \n",
    "<cite data-cite=\"ilu0jl\"><a href=\"siamfa.html#ilu0jl\">(Cov21)</cite> and\n",
    "__IncompleteLU.jl__ \n",
    "<cite data-cite=\"ilujl\"><a href=\"siamfa.html#ilujl\">(Sto21)</cite>\n",
    "implement incomplete LU factorizations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3.4: Computing an Approximate Newton Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.4.1: Jacobian-Vector Products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For nonlinear\n",
    "equations, the Jacobian-vector product is easy to approximate with\n",
    "a forward difference directional derivative.\n",
    "The forward difference directional derivative at $x$ in the direction\n",
    "$w$ is\n",
    "$$\n",
    "D_h \\mf(\\vx:\\vw) = \\left\\{\n",
    "\\begin{array}{ll}\n",
    "0, & \\vw = 0,\\\\\n",
    "\\\\\n",
    "\\| \\vw \\|\n",
    "\\dfrac{\\mf(\\vx + h \\vw /\\| \\vw \\| ) - \\mf(\\vx)}{h}, & \\vw \\ne 0.\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "The scaling is important.\n",
    "We first scale $\\vw$ to be a unit vector and\n",
    "take a numerical directional derivative in the direction $\\vw/\\| \\vw \\|$.\n",
    "If $h$ is roughly the square root\n",
    "of the error in $\\mf$, we use a difference increment in\n",
    "the forward difference to make sure that the appropriate low-order bits\n",
    "of $\\vx$ are perturbed.\n",
    "The same scaling was used in the\n",
    "forward difference Jacobian in [Chapter 2](SIAMFANLCh2.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.4.2: Preconditioning for Nonlinear Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Newton-Krylov solver __nsoli.jl__\n",
    "allows you to apply a preconditioner on\n",
    "either the left or right by a preconditioner-vector\n",
    "product function. This is simpler and more direct that the approach\n",
    "from \n",
    "<cite data-cite=\"ctk:newton\"><a href=\"siamfa.html#ctk:newton\">(Kel03)</cite>,\n",
    "where the matlab code\n",
    "expects you to incorporate preconditioning into\n",
    "$\\mf$. We explain that approach later in this section.\n",
    "\n",
    "We will warn you now, and go in to details later,\n",
    "that only right preconditioning makes sense for $\\ptc$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Left or Right Preconditioning?\n",
    "\n",
    "Right preconditioning has a\n",
    "significant advantage in Newton-Krylov methods because the residual\n",
    "is the same residual used in the inexact Newton condition. If one uses\n",
    "left preconditioning one may terminate on a Newton step that fails to\n",
    "satisfy the inexact Newton condition for the unpreconditioned problem and\n",
    "affect the performance of the line search.\n",
    "\n",
    "__Left preconditioning__\n",
    "will terminate the linear iteration when\n",
    "$\\| \\mP \\mf'(\\vx) \\vd + \\mP \\mf(\\vx) \\|$\n",
    "is small. If\n",
    "$\\mP$ is a good approximation to $\\mf'(\\vx^*)^{-1}$, then\n",
    "$$\n",
    "\\vd \\approx - \\mP \\mf'(\\vx^*) \\ve \\approx - \\ve\n",
    "$$\n",
    "and so one might expect a better Newton step which\n",
    "captures the actual error. However, the meaning of\n",
    "the inexact Newton condition for left preconditioning is not\n",
    "completely clear.\n",
    "\n",
    "On the\n",
    "other hand, if one uses \n",
    "__right preconditioning__, then the linear iteration will\n",
    "terminate when\n",
    "$\\| \\mf'(\\vx) \\vd + \\mf(\\vx) \\|$ is\n",
    "small, reflecting the inexact Newton condition and\n",
    "responding to the\n",
    "problem statement \n",
    "\"Make the linear residual small\",\n",
    "which corresponds exactly to the inexact Newton condition\n",
    " and __Theorem 1.4__ for\n",
    "convergence of inexact Newton methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preconditioning the Nonlinear Problem\n",
    "\n",
    "One can sometime code the combination of preconditioner and\n",
    "nonlinear residual more efficiently that doing them separately.\n",
    "To precondition the equation for the Newton step from the left, one\n",
    "would\n",
    "simply apply __nsoli.jl__ to the preconditioned nonlinear problem\n",
    "$$\n",
    "\\mf_P(\\vx) = \\mP \\mf(\\vx) = 0.\n",
    "$$\n",
    "The equation for the Newton step for $\\mf_P$ is\n",
    "$$\n",
    "\\mf_P'(\\vx) \\vd = \\mP \\mf'(\\vx) \\vd = - \\mf_P (\\vx) = - \\mP \\mf(\\vx),\n",
    "$$\n",
    "which is the left-preconditioned equation for the Newton direction\n",
    "for $\\mf$.\n",
    "\n",
    "Left preconditioning makes more sense if one is preconditioning the\n",
    "nonlinear problem. The reason for this is that the line search is applied\n",
    "to the preconditioned problem, which is not the case if one preconditions\n",
    "the linear solver. The examples in \n",
    "<cite data-cite=\"ctk:newton\"><a href=\"siamfa.html#ctk:newton\">(Kel03)</cite>\n",
    "make left preconditioning look a bit too good for exactly this reason.\n",
    "We will revisit this issue in the examples in __section 3.6__.\n",
    "\n",
    "In nonlinear right preconditioning\n",
    "we set $\\vx = \\mP {\\vy}$ and solve\n",
    "$$\n",
    "\\mf_P(\\vy) = \\mf(\\mP \\vy) = 0\n",
    "$$\n",
    "with Newton's method. The equation for the direction\n",
    "${\\tilde \\vd}$ is\n",
    "$$\n",
    "\\mf_P'(\\vy) {\\tilde \\vd}\n",
    "= \\mf'(\\mP \\vy) \\mP {\\tilde \\vd} = -\\mf_P(\\vy) = - \\mf(\\mP \\vy),\n",
    "$$\n",
    "which is exactly the right-preconditioned equation for the step.\n",
    "Hence right preconditioning for the nonlinear problem produces the\n",
    "same iteration as solving the original problem and using right\n",
    "preconditioning for the linear equation for the Newton direction.\n",
    "\n",
    "Preconditioning for $\\ptc$ is a different story and we cover that in\n",
    "__section 3.4__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.4.3: Choosing the Forcing Term"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One should take care to cho0se a forcing term that is not too small at\n",
    "the beginning of the iteration. For example, setting $\\eta = 10^{-8}$\n",
    "for the entire iteration will lead to a low number of nonlinear iterations.\n",
    "However, one will be taking far to much time on linear solves early on.\n",
    "If one wants to fix $\\eta$, the author's favorite approach, a more sensible\n",
    "value is $\\eta = .1$, which is the default in __nsoli.jl}__ Once again,\n",
    "$\\ptc$ is different and a smaller forcing term, especially at the beginning,\n",
    "may be needed to track the dynamics.\n",
    "Having said all that, we must also discuss a very clever alternative for\n",
    "Newton's method.\n",
    "\n",
    "The Eisenstat-Walker (EW)\n",
    "approach from <cite data-cite=\"homerstan\"><a href=\"siamfa.html#homerstan\">(EW94)</cite>\n",
    "changes the forcing term $\\eta$\n",
    " as\n",
    "the nonlinear iteration progresses. The formula is complex\n",
    "and motivated by a lengthy story, which we condense from\n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>.\n",
    "The overall goal in <cite data-cite=\"homerstan\"><a href=\"siamfa.html#homerstan\">(EW94)</cite>\n",
    "is to solve the linear\n",
    "equation for the Newton step to just enough precision to\n",
    "make good progress when far from a solution, but also to\n",
    "obtain quadratic convergence when near a solution. One might\n",
    "base a choice of $\\eta$ on residual norms; one way to do\n",
    "this is\n",
    "$$\n",
    "\\eta_n^{Res} = \\gamma \\| \\mf(\\vx_n)\\|^2/\\| F(\\vx_{n-1})\\|^2,\n",
    "$$\n",
    "where $\\gamma \\in (0,1]$ is a parameter. If $\\eta_n^{Res}$ is bounded\n",
    "away from $1$ for the entire iteration, the choice $\\eta_n = \\eta_n^{Res}$\n",
    "will do the job, assuming we make a good choice for $\\eta_0$. To\n",
    "make sure that $\\eta_n$ stays well away from $1$, we can simply\n",
    "limit its maximum size.\n",
    "Of course, if $\\eta_n$ is too small in the early stage of\n",
    "the iteration,\n",
    "then the linear equation for the Newton step can be solved\n",
    "to far more precision than is really needed. To protect against\n",
    "__oversolving__, a method of\n",
    "__safeguarding__ was proposed in <cite data-cite=\"homerstan\"><a href=\"siamfa.html#homerstan\">(EW94)</cite>\n",
    "to avoid volatile decreases in $\\eta_n$. The idea is that if\n",
    "$\\eta_{n-1}$ is sufficiently large, we do not let $\\eta_n$ decrease\n",
    "by too much; <cite data-cite=\"homerstan\"><a href=\"siamfa.html#homerstan\">(EW94)</cite>\n",
    "suggests limiting the decrease to\n",
    "a factor of $\\eta_{n-1}$.\n",
    "    \n",
    "After taking all this into account, one finally arrives at \n",
    "<cite data-cite=\"ctk:roots\"><a href=\"siamfa.html#ctk:roots\">(Kel95)</cite>    \n",
    "$$\n",
    "\\eta_n = \\min( \\eta_{max}, \\max(\\eta_n^{Safe},\n",
    "0.5 \\tau_t/\\| \\mf(\\vx_n)\\| ) ).\n",
    "$$\n",
    "The term\n",
    "$$\n",
    "\\tau_t = \\tau_a + \\tau_r \\| \\mf(\\vx_0) \\|\n",
    "$$\n",
    "is the termination tolerance for the nonlinear iteration and is\n",
    "included in the formula to prevent oversolving on the final iteration.\n",
    "$\\eta_{max}$ is an upper limit on the forcing term and\n",
    "$$\n",
    "\\eta_n^{Safe} = \\left\\{\n",
    "\\begin{array}{ll}\n",
    "\\eta_{max}, & n = 0,\\\\\n",
    "\\\\\n",
    "\\min(\\eta_{max}, \\eta_n^{Res} ), & n > 0, \\gamma  \\eta_{n-1}^2 \\le 0.1,\\\\\n",
    "\\\\\n",
    "\\min(\\eta_{max}, \\max(\\eta_n^{Res}, \\gamma  \\eta_{n-1}^2) ), &\n",
    "n > 0, \\gamma  \\eta_{n-1}^2 > 0.1,\\\\\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "    \n",
    "In <cite data-cite=\"homerstan\"><a href=\"siamfa.html#homerstan\">(EW94)</cite> \n",
    "    $\\gamma=0.9$ and $\\eta_{max} = 0.9999$ are used. In __nsoli.jl__\n",
    "we set $\\gamma = .9$. You can specify $\\eta_{max}$ in the call to\n",
    "__nsoli.jl__.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3.5: Pseudo-Transient Continuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The considerations from [Chapter 2](SIAMFANLCh2.ipynb) are\n",
    "important for all choices of linear solver.\n",
    "There are a few more things to think about when using Krylov solvers to\n",
    "compute the step in $\\ptc$. The first is the forcing term. Unlike\n",
    "Newton's method, when the Eisenstat-Walker method permits one to use\n",
    "a large forcing term early in the iteration, $\\ptc$ must accurately capture\n",
    "the transient dynamics early, and therefore one should chose a small forcing\n",
    "term to do that.\n",
    "\n",
    "Preconditioning is also different. Recall that the linear equation for the\n",
    "$\\ptc$ step is\n",
    "$$\n",
    "( \\delta_n^{-1} \\mv + \\mf'(\\vx_n) ) \\vs = - \\mf(\\vx_n)\n",
    "$$\n",
    "and the inexact Newton condition is\n",
    "$$\n",
    "\\| (\\delta^{-1} \\mv + \\mf'(\\vx) ) \\vs + \\mf(\\vx) \\| \\le \\eta \\| \\mf(\\vx) \\|.\n",
    "$$\n",
    "The coefficient matrix includes $\\delta$ and one might think that the\n",
    "preconditioner should be aware of $\\delta$ as well. We will explore\n",
    "that question later. More importantly, satisfaction of the inexact Newton\n",
    "condition is very important if one wants to follow the dynamics accurately\n",
    "in the early stages of the iteration. Hence one should precondition\n",
    "the linear equation from the right.\n",
    "\n",
    "The forcing term is also important for following the dynamics in the\n",
    "early stages. Using a forcing term that is too large can cause a jump\n",
    "to a different stable branch that the one you want.\n",
    "\n",
    "Left preconditioning and nonlinear\n",
    "preconditioning are not good ideas\n",
    "for $\\ptc$.\n",
    "There is no reason for the stability of the preconditioned initial\n",
    "value problem to be the same as that for the original version.\n",
    "For example,\n",
    "if one uses nonlinear right preconditioning and replaces $\\mf(\\vx)$ with\n",
    "$\\mf \\mP \\vy)$, the corresponding time-dependent problem would be\n",
    "$$\n",
    "\\mP \\frac{d \\vx}{dt} = - \\mv^{-1} \\mf(\\mP \\vy), \\  \\vy(0) = \\mP \\vx_0,\n",
    "$$\n",
    "which could have completely different stability properties.\n",
    "\n",
    "The linear equation for the $\\ptc$ step depends on $\\delta$. One may want\n",
    "the preconditioner to do that as well. __ptcsoli.jl__ allows you to \n",
    "do that and we give an example below.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3.6: What Can Go Wrong?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any problem from \n",
    "[Chapter 1](SIAMFANLCh1.ipynb)\n",
    "or [Chapter 2](SIAMFANLCh2.ipynb), of course, can arise if\n",
    "you solve linear systems by iteration. There are a few problems\n",
    "that are unique to Newton iterative methods.\n",
    "The symptoms of these problems are unexpectedly slow convergence\n",
    "or even failure/stagnation of the nonlinear iteration.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.6.1: Failure of the Inner Iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the linear iteration does not satisfy the inexact Newton\n",
    "condition and the limit on linear iterations has been reached,\n",
    "a sensible response is to warn the user and return the step\n",
    "to the nonlinear iteration. Most codes, including __nsoli.jl__,\n",
    "do this. While it is likely that the nonlinear\n",
    "iteration will continue to make progress, convergence is not\n",
    "certain and one may have to allow the linear solver more iterations,\n",
    "use a different linear solver, or, in extreme cases, find enough\n",
    "storage to use a direct solver.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.6.2: Loss of Orthogonality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GMRES exploits orthogonality of the Krylov basis to\n",
    "estimate the residual.\n",
    "In finite-precision arithmetic this orthogonality\n",
    "can be lost and the estimate of the residual in the iteration\n",
    "can be poor. The iteration could terminate prematurely because\n",
    "the estimated residual satisfies the inexact Newton condition while the\n",
    "true residual does not. This is a much more subtle problem\n",
    "than failure to converge because the linear solver can\n",
    "report success but return an inaccurate and useless step.\n",
    "The GMRES code __kl_gmres.jl__ has several orthogonalization\n",
    "methods as options, but __nsoli.jl__ and __ptcsoli.jl__ take\n",
    "the fastest and most robust of these options, which is classical\n",
    "Gram-Schmidt orthogonalization repeated twice. This is the approach\n",
    "used in most advanced codes such as Trilinos\n",
    "<cite data-cite=\"trilinos\"><a href=\"siamfa.html#trilinos\">(HBH<sup>+</sup>05)</cite>,\n",
    "for example.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3.7: Newton-Krylov Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we consider examples of the the choice of the forcing term,\n",
    "left vs right preconditioning, how preconditioning affects \\ptc, and how\n",
    "low storage Krylov methods affect the nonlinear iteration. We compare the\n",
    "alternatives with residual histories, as we did in [Chapter 2](SIAMFANLCh2.ipynb),\n",
    "but compare not only residual norm as a function of the nonlinear iteration\n",
    "count, but also as a function of the number of Jacobian-vector products.\n",
    "This latter comparison better illustrates the linear algebra cost and\n",
    "therefore the effects of preconditioning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.7.1: Choice of the ForcingTerm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we explore how the choice of $\\eta$ affects\n",
    "convergence and cost. We use the default {\\bf nsoli.jl} choice of\n",
    "a fixed $\\eta=.1$ and the Eisenstat-Walker choice with two values of\n",
    "$\\eta_{max}$. \n",
    "In __Figure 3.1__, we show the results for the\n",
    "H-equation, a well-conditioned\n",
    "problem with a good initial iterate. In this case\n",
    "the choice of forcing term makes little difference.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nk_heq_forcing();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In __Figure 3.2__ we show results for the\n",
    "convection-diffusion problem from\n",
    "[Chapter 2](SIAMFANLCh2.ipynb) with no preconditioning or\n",
    "right preconditioning. We can exploit the regular grid by using a\n",
    "__fast Poisson solver__ $\\mP$ as a preconditioner. Our preconditioner __fish2d.jl__\n",
    "uses the Julia package __FFTW.jl__\n",
    "<cite data-cite=\"fftwjl\"><a href=\"siamfa.html#fftwjl\">(Joh20)</cite>\n",
    "to solve the discrete form of\n",
    "$$\n",
    "-\\nabla^2 w = u\n",
    "$$\n",
    "with homogeneous Dirichlet boundary conditions\n",
    "to return $\\vw = \\mP  \\vu$. \n",
    "\n",
    "The iteration with the Eisenstat-Walker forcing term with \n",
    "a small value $\\eta_{max}=.1$ takes fewer nonlinear iterations, but\n",
    "the difference between that choice and fixed $\\eta$ largely vanishes\n",
    "in terms of Jacobian-vector products. This is because Eisenstat-Walker takes\n",
    "more linear iterations in the terminal phase of the nonlinear iteration.\n",
    "Without preconditioning,\n",
    "the larger choice of $\\eta = .5$ struggles early in the iteration because the\n",
    "large forcing term results in a direction that causes the line search\n",
    "to work harder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nk_pde_forcing();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.7.2: Left vs Right Preconditioning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We give one example of how right and left preconditioning differ\n",
    "for the convection-diffusion example when we use the Eisenstat-Walker\n",
    "forcing term with $\\eta_{max}=.9$. This large value of $\\eta_{max}$ can\n",
    "be useful if the initial iterate is poor and an accurate step is not\n",
    "needed, but only if one preconditions from the right.\n",
    "The benchmark results for this example are in the notebook results in\n",
    "the notebook section. The results for left preconditioning and\n",
    "$\\eta = .9$ are not good.\n",
    "\n",
    "\n",
    "    \n",
    "In this example we precondition the linear equation for the Newton direction\n",
    "with both left and right preconditioning.\n",
    "This means that the line search is based on the unpreconditioned residual.\n",
    "This is a case where left preconditioning can do poorly because it does\n",
    "not directly use the inexact Newton condition for the original\n",
    "nonlinear problem to terminate the linear iteration. As you can see from __Figure 3.3__ the\n",
    "iteration with left preconditioning spends much more time in the early\n",
    "phase of the iteration. An examination of the __stats.iarm__ field in\n",
    "the output shows that that the left preconditioned iteration is in the\n",
    "line search for those iterations. It finally converges, but there is\n",
    "no guarantee of this because the theory is based on satisfying the\n",
    "inexact Newton condition with a value of $\\eta$ that is bounded away\n",
    "from $1$, and with left preconditioning the soluition of the linearized problem is not\n",
    "guaranteed to do that.\n",
    "    \n",
    "On the other hand, if we precondition the nonlinear problem from the\n",
    "left, then the line search uses the preconditioned residual and the\n",
    "nonlinear convergence is much better. One must be careful in interpreting\n",
    "the figure because the nonlinearly preconditioned residual history\n",
    "(labeled Left-NL) is the preconditioned residual norm and not measuring\n",
    "the same thing as the other two curves. Having said that, the convergence\n",
    "of the nonlinearly preconditioned iteration is fast and takes slightly fewer function\n",
    "evaluations and iterations that the right preconditioned iteration.\n",
    "\n",
    "The codes for this example are in the file\n",
    "__src/TestProblems/Systems/EllipticPDE.jl__. The nonlinear\n",
    "function for the preconditioned nonlinear problem is \n",
    "__hardleft!__. This function calls the residual for the equation\n",
    "__pdeF!__ and follows that with a call to the preconditioner\n",
    "__Pfish2d__ which uses our fast Poisson solver __fish2d.jl__.\n",
    "\n",
    "```\n",
    "\"\"\"\n",
    "hardleft!(FV, u, pdata)\n",
    "Convection-diffusion equation with left preconditioning hard-wired in\n",
    "\n",
    "\"\"\"\n",
    "function hardleft!(FV, u, pdata)\n",
    "fdata=pdata.fdata\n",
    "# Call the nonlinear function\n",
    "FV = pdeF!(FV,u,pdata)\n",
    "# and apply the preconditioner.\n",
    "FV .= Pfish2d(FV,fdata)\n",
    "return FV\n",
    "end\n",
    "```\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sideout=nk_pde_side();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.7.3:  Preconditioning Pseudo-transient Continuation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The examples in this section are for the buckling beam. We use\n",
    "the function __FBeam!.jl__ for the residual and precondition with\n",
    "a fast (tridiagonal) solver for the high order term. We consider two\n",
    "choices, both use the tridiagonal matrix $\\md_2$ for the second derivative\n",
    "operator homogeneous Dirichlet boundary conditions. We compare preconditioning\n",
    "with an application of a preconditioner that uses the current value\n",
    "of $\\delta$, $(\\md_2 + \\delta^{-1} \\mi)^{-1}$, with one that does not\n",
    "$\\md_2^{-1}$. The functions to apply the preconditioner-vector product\n",
    "are simple.  __ptcsoli.jl__ allows you to either use $\\delta$ in the\n",
    "preconditioner or not.\n",
    "\n",
    "The two functions we list below are contained in\n",
    "SIAMFANLEquations.jl/src/Examples/ptciBeam.jl and show one\n",
    "approach to including $\\delta$ in the preconditioner.\n",
    "The tridiagonal matrix ```D2``` is part of the precomputed data for\n",
    "this problem and the step ```delta``` is stored in a preallocated array.\n",
    "We store preallocated data in a named tuple in Julia. This is an \n",
    "immutable structure and one cannot change scalars in such a structure. However,\n",
    "one can put an array in a named tuple and change its contents. This is how\n",
    "we manage to inform the preconditioner of the pseudo timestep ```delta```.\n",
    "\n",
    "```\n",
    "\"\"\"\n",
    "ptvbeampdt(v, x, bdata)\n",
    "\n",
    "Precondition buckling beam problem with delta-aware preconditioner.\n",
    "\"\"\"\n",
    "function ptvbeampdt(v, x, bdata)\n",
    "    delta = bdata.pdtval[1]\n",
    "    J = bdata.D2 + (1.0 / delta) * I\n",
    "    ptv = J \\ v\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "ptvbeamp(v, x, bdata)\n",
    "\n",
    "Precondition buckling beam problem with inverse of high-order term.\n",
    "\"\"\"\n",
    "function ptvbeam(v, x, bdata)\n",
    "    J = bdata.D2\n",
    "    ptv = J \\ v\n",
    "end\n",
    "```\n",
    "In __Figure 3.4__ we plot the residual histories\n",
    "using right preconditioning with\n",
    "both choices of preconditioners and the history from [Chapter 2](SIAMFANLCh2.ipynb)\n",
    "that uses a direct tridiagonal solve. As you can see, letting the\n",
    "preconditioner know about $\\delta$ makes little difference in the\n",
    "results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptcCompare();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We advise against left preconditioning for several reasons, and this\n",
    "example illustrates one of them. Here we apply left preconditioning\n",
    "the the buckling beam problem. In __Figure 3.5__ we plot\n",
    "the residual histories on the left and the solutions on the right.\n",
    "While the residual histories look fine\n",
    "for both preconditioners, the solutions are not the same! The preconditioner\n",
    "that is not aware of the value of $\\delta$ fails to find the non-negative\n",
    "solution which is the correct limit of the time-dependent solution. While\n",
    "the non-positive solution is a stable steady-state, it is not the correct\n",
    "solution for the initial data. The preconditioner has let the iteration\n",
    "drift onto the incorrect trajectory. The fact that the preconditioner\n",
    "that is aware of $\\delta$ found the correct trajectory is likely an accident."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LptcCompare();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.6.4 Low Storage Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you cannot store enough Krylov vectors for GMRES to converge well,\n",
    "then low storage Krylov methods could help. None of the\n",
    "problems in this book are large enough or poorly conditioned enough\n",
    "for GMRES to have convergence problems. We'll consider three examples in\n",
    "this section, two well-conditioned ones and a poorly conditioned one.\n",
    "\n",
    "In all the examples we used the Eisenstat-Walker forcing term with\n",
    "$\\eta_{max} = .1$. The two low-storage methods are BiCGSTAB and\n",
    "GMRES(2). To inform __nsoli.jl__ that we are using GMRES(2) we simply\n",
    "allocate only three vectors and set the kwarg ```lmaxit = 40``` to take 40\n",
    "GMRES(2) iterations.\n",
    "\n",
    "In the case of the H-equation\n",
    "example, the call looks like\n",
    "\n",
    "```Julia\n",
    "    # Set up the solve\n",
    "    n = 100\n",
    "    #\n",
    "    # Preallocated storage\n",
    "    #\n",
    "    u0 = zeros(n)\n",
    "    FS = zeros(n)\n",
    "    # Allocate three vectors for GMRES(2)\n",
    "    FPSS = ones(n, 3)\n",
    "    #\n",
    "    # Get organized\n",
    "    #\n",
    "    c = 0.9\n",
    "    atol = 1.e-10\n",
    "    rtol = 1.e-10\n",
    "    hdata = heqinit(u0, c)\n",
    "    koutgs = nsoli( heqf!, u0, FS, FPSS;\n",
    "        pdata = hdata, rtol = rtol, atol = atol,\n",
    "        lmaxit = 40, eta = .1, fixedeta = false,\n",
    "    )\n",
    "\n",
    "```\n",
    "\n",
    "We begin with the H-equation example on a 100 point grid with $c=.9$. The figure\n",
    "shows the residual history plotted against\n",
    "nonlinear iterations and also against Jacobian-vector products.\n",
    "Note that\n",
    "the residual histories on the left are very similar. The plot on the right\n",
    "shows that, as you might expect, GMRES uses fewer calls to the function.\n",
    "BiCGSTAB, which has two Jacobian-vector products for each linear iteration,\n",
    "and GMRES(2), which should take more linear iterations, are not much worse.\n",
    "This problem is very well-conditioned and the initial iterate $u_0=1$ is\n",
    "good, so the nonlinear iteration performs well and the linear iteration\n",
    "does not have to work very hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nk_heq_bicgstab();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next example is the convection-diffusion problem. We precondition\n",
    "with the fast Poisson solver as we did in the discussion of left vs right preconditioning.\n",
    "We use a $31 \\times 31$ point mesh and $u0=0$ as the initial iterate.\n",
    "While this problem is well-conditioned, the initial iterate is poor enough\n",
    "to require a line search in the first two iterations.\n",
    "\n",
    "As was the case for the H-equation, the plot on the left shows that the\n",
    "number of nonlinear iterations was the same for all of the linear solvers.\n",
    "The plot on the right shows that GMRES takes fewer Jacobian-vector products\n",
    "throughout the iteration, both in the line search phase and in the terminal\n",
    "phase where the Eisenstat-Walker forcing term becomes small. GMRES is\n",
    "better by roughly 50\\% that the low-storage methods.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nk_pde_bicgstab();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure below we report on the same experiment\n",
    "using the unpreconditioned convection-diffusion example. The lack of\n",
    "preconditioning significantly increases the cost of the solve, as\n",
    "the plot on the right clearly shows. The performance of GMRES(2) is\n",
    "poor enough to even affect the nonlinear iteration, whereas the\n",
    "nonlinear iteration with full GMRES and BiCGSTAB is similar to\n",
    "the preconditioned case. One can see on the plot on the left that\n",
    "GMRES(2) has trouble finding a good Newton direction in the early\n",
    "phase of the iteration and converges more slowly in the terminal\n",
    "phase. In fact, the GMRES(2) iteration failed to satisfy the\n",
    "inexact Newton condition five times, twice in the early phase\n",
    "and three times in the terminal phase. One can see this by looking at\n",
    "the\n",
    "```Julia stats.ikfail ``` field in the output. The warning\n",
    "message\n",
    "```Julia\n",
    "Linear solver did not meet termination criterion at least once.\n",
    "    This does not mean the nonlinear solver will fail. lmaxit= 100\n",
    "```\n",
    "only appears once when you run the solver. As you can see, the nonlinear\n",
    "iteration did converge, but the failure of the inexact Newton condition\n",
    "did cost a few iterations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newton-Krylov: Linear solver did not meet termination criterion at least once.\n",
      "               This does not mean the nonlinear solver will fail. lmaxit= 100\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1×9 adjoint(::Vector{Int64}) with eltype Int64:\n",
       " 0  1  1  0  0  1  1  1  0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nopcout=nk_nopc_pde_bicgstab();\n",
    "# Get the stats from the GMRES(2) run and see when the linear solver failed\n",
    "nopcout.poutgl.stats.ikfail'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next notebook = [Section 3.8: Solvers for Chapter 3](SIAMFANLCh3s.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.11.0",
   "language": "julia",
   "name": "julia-1.11"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}