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Iterative method

An iterative method is a mathematical procedure that generates a sequence of increasingly accurate approximations to the solution of a problem, such as solving a system of linear equations Ax = b, by starting from an initial guess and repeatedly applying a transformation until the residual or error falls below a specified tolerance.^[1] These methods are fundamental in numerical analysis, particularly for large-scale, sparse systems where direct solvers like Gaussian elimination become inefficient due to high computational and memory demands.^[2] Unlike direct methods, which yield exact solutions in a finite number of steps assuming infinite precision, iterative methods produce approximations that converge asymptotically to the true solution, often requiring monitoring of convergence criteria such as the L_\infty norm of the residual.^[3] Iterative methods can be broadly classified into stationary and non-stationary categories. Stationary methods, such as the Jacobi method and Gauss-Seidel method, employ a fixed iteration matrix derived from a splitting of the coefficient matrix A = M - N, where the update rule is x^{(k+1)} = M^{-1}(b + N x^{(k)}), and convergence depends on the spectral radius of the iteration matrix being less than 1.^[1] The Jacobi method updates all components simultaneously using values from the previous iteration, making it suitable for parallel computation, while Gauss-Seidel incorporates newly computed values immediately, typically converging twice as fast as Jacobi for the same systems.^[2] Non-stationary methods, including Krylov subspace techniques like the conjugate gradient (CG) method, adapt the iteration process and are particularly effective for symmetric positive definite matrices, often requiring fewer iterations—on the order of \sqrt{n} for n \times n systems—when preconditioned.^[2] Convergence of iterative methods is influenced by matrix properties, such as diagonal dominance or positive definiteness, and can be accelerated through preconditioning, which effectively reduces the condition number of the system.^[1] These methods find widespread applications in solving partial differential equations, optimization problems, and scientific simulations, where systems can involve millions of unknowns, prioritizing efficiency over exactness in finite arithmetic.^[3]

General Principles

Definition of Iterative Methods

Iterative methods constitute a broad class of algorithms in numerical mathematics that produce a sequence of approximations to the solution of a problem by starting from an initial estimate and iteratively refining it through repeated application of a prescribed transformation until the approximations satisfy a predefined accuracy criterion.^[4] These methods are particularly valuable for addressing complex computational challenges where exact solutions are either unattainable or inefficient to compute directly.^[5] In contrast to direct methods, which yield exact solutions in a finite number of arithmetic operations—such as Gaussian elimination for linear systems—iterative methods generate successively better approximations that converge asymptotically to the true solution but do not guarantee exactness within finite steps.^[5] This approximate nature makes iterative methods especially suitable for large-scale problems, including those involving massive matrices or high-dimensional spaces, where direct approaches would demand prohibitive storage and time due to factors like matrix fill-in or cubic complexity.^[5] The generic framework of an iterative method can be expressed as

\mathbf{x}_{k+1} = G(\mathbf{x}_k),

where \mathbf{x}_k represents the approximation at the k-th iteration and G is the iteration function that maps the current estimate to the next.^[4] Understanding these methods requires familiarity with the convergence properties of sequences in real or complex analysis, where the limit of the sequence \{\mathbf{x}_k\} is analyzed to confirm approach to the solution under suitable conditions on G.^[4] Iterative methods are employed across diverse domains, including the root-finding for nonlinear equations f(\mathbf{x}) = \mathbf{0}, the solution of large sparse linear systems A\mathbf{x} = \mathbf{b}, and the minimization of objective functions in optimization problems.^[6]^[5]^[7] A foundational illustration is fixed-point iteration, which reformulates the problem as seeking a point \mathbf{x}^* such that \mathbf{x}^* = G(\mathbf{x}^*).^[4]

Fixed-Point Iteration

Fixed-point iteration is a fundamental technique in numerical analysis for solving equations by reformulating them into a fixed-point form. To solve an equation f(x) = 0, one rearranges it as x = g(x), where g is a function chosen such that any fixed point p satisfying g(p) = p corresponds to a root of the original equation.^[8] The iteration begins with an initial approximation x_0 and generates subsequent iterates via the recurrence x_{n+1} = g(x_n). This process continues until a stopping criterion is met, such as |x_{n+1} - x_n| < \epsilon for a small tolerance \epsilon > 0, indicating sufficient proximity to the fixed point.^[8] The selection of g is crucial for ensuring efficient convergence; ideally, g should be a contraction mapping, meaning it satisfies a Lipschitz condition with constant k < 1, which promotes rapid approach to the fixed point.^[8] The Banach fixed-point theorem provides a theoretical foundation for convergence. In a complete metric space D, if g: D \to D is a contraction with \|g(x) - g(y)\| \leq q \|x - y\| for some q < 1 and all x, y \in D, then g has a unique fixed point x^* \in D, and the iteration x_{n+1} = g(x_n) converges to x^* from any initial x_0 \in D.^[9] Error analysis for fixed-point iteration typically reveals linear convergence when |g'(p)| < 1, where the asymptotic error constant is |g'(p)|, meaning the error e_{n+1} \approx |g'(p)| e_n with e_n = |x_n - p|. Quadratic convergence occurs if g'(p) = 0 and g''(p) \neq 0, yielding e_{n+1} \approx \frac{|g''(p)|}{2} e_n^2, which accelerates the reduction in error.^[8]^[10]

Convergence Criteria

Convergence of iterative methods is broadly classified into local and global types. Local convergence occurs when the iteration converges to the fixed point provided the initial guess is sufficiently close to the solution, relying on the behavior of the iteration function near that point. In contrast, global convergence guarantees that the method reaches the fixed point from a wide range of starting points, often requiring the iteration function to satisfy stronger conditions like being a contraction mapping on the entire domain.^[11] For fixed-point iterations of the form x_{k+1} = g(x_k), a fixed point x^* where g(x^*) = x^* is attractive if |g'(x^*)| < 1, ensuring local convergence with the error decreasing as the iterates approach x^*. If g'(x^*) = 0, the fixed point is superattractive, leading to faster quadratic convergence near the solution. These conditions stem from analyzing the derivative's magnitude, which determines the local stability of the fixed point.^[12] In the linear case, where the iteration is x_{k+1} = G x_k + c, convergence to the unique fixed point requires the spectral radius \rho(G) < 1, with \rho(G) defined as the maximum absolute eigenvalue of G. This condition is necessary and sufficient for asymptotic convergence regardless of the initial guess, as the error e_{k+1} = G e_k satisfies \|e_k\| \to 0 if and only if all eigenvalues of G lie inside the unit circle. When \rho(G) \geq 1, the iteration may diverge or oscillate without bound, failing to produce a solution; for instance, if an eigenvalue has magnitude greater than 1, the error amplifies in that direction.^[13] Practical tests for convergence include evaluating |g'(x)| < 1 at points near the suspected solution to assess local attractiveness, providing an indicator before full iteration. To accelerate linearly convergent iterations, Aitken's \delta^2-process estimates the limit using three consecutive iterates as \hat{x}_n = x_n - \frac{(x_n - x_{n-1})^2}{x_n - 2x_{n-1} + x_{n-2}}, assuming a constant error ratio |r| < 1; this extrapolation reduces the number of iterations needed for a given accuracy.^[14] The rate of convergence quantifies how quickly the error diminishes, defined by order p > 0 if the error satisfies e_{k+1} \approx C e_k^p for some constant C > 0, with asymptotic behavior as k \to \infty. For p = 1 and C < 1, convergence is linear, typical of simple fixed-point iterations where the error reduces by a fixed factor each step. Quadratic convergence (p = 2) arises in methods like Newton's, where errors square, enabling rapid refinement once close to the solution, though it requires differentiability and a good initial guess. Higher orders are possible but rarer in standard iterative schemes.^[15]

Methods for Linear Systems

Stationary Iterative Methods

Stationary iterative methods constitute a fundamental class of algorithms for solving systems of linear equations Ax = b, where A is an n \times n matrix, characterized by a fixed iteration matrix that does not vary across iterations. These methods express the solution as a fixed point of a linear iteration x^{k+1} = G x^k + c, with convergence guaranteed if the spectral radius \rho(G) < 1.^[16]^[17] The general formulation arises from matrix splitting A = M - N, where M is nonsingular and chosen for computational efficiency, such as being diagonal or triangular. The iteration then becomes

x^{k+1} = M^{-1} N x^k + M^{-1} b,

with iteration matrix G = M^{-1} N. The error propagates as e^{k+1} = G e^k, where e^k = x^k - x, and asymptotic convergence rate is determined by \rho(G).^[17]^[18] The Jacobi method, introduced by Carl Gustav Jacob Jacobi in 1845, employs M = D, the diagonal part of A, yielding

x_i^{k+1} = \frac{1}{a_{ii}} \left( b_i - \sum_{j \neq i} a_{ij} x_j^k \right).

This method is inherently parallelizable, as each component update depends only on values from the previous iteration. It converges for strictly diagonally dominant matrices, where |a_{ii}| > \sum_{j \neq i} |a_{ij}| for all i, with \rho(G_J) \leq \max_i \sum_{j \neq i} |a_{ij}| / |a_{ii}| < 1.^[19]^[17]^[20] The Gauss-Seidel method, proposed by Ludwig Seidel in 1874 based on earlier ideas from Carl Friedrich Gauss in 1823, uses M = D + L, where L is the strict lower triangular part of A. The iteration updates components sequentially:

x_i^{k+1} = \frac{1}{a_{ii}} \left( b_i - \sum_{j < i} a_{ij} x_j^{k+1} - \sum_{j > i} a_{ij} x_j^k \right).

By incorporating newly computed values immediately, it typically converges faster than Jacobi for the same class of matrices, including diagonally dominant and symmetric positive definite ones, with \rho(G_{GS}) \leq [\rho(G_J)]^2 in many cases.^[19]^[17]^[21] Successive over-relaxation (SOR), developed by Stanley Frankel in 1950 and analyzed by David M. Young in his 1950 thesis (published 1954), introduces a relaxation parameter \omega to accelerate convergence. With M_\omega = \frac{1}{\omega} (D + \omega L), the iteration is

x^{k+1} = (D + \omega L)^{-1} \left[ (1 - \omega) D x^k + \omega (b - U x^k) \right],

equivalent to a convex combination of Gauss-Seidel and identity updates. For symmetric positive definite A, SOR converges for $0 < \omega < 2, and the optimal \omega_b = \frac{2}{1 + \sqrt{1 - \rho(G_J)^2}} minimizes \rho(G_\omega), achieving rates superior to Gauss-Seidel. For model problems like the discrete Laplace equation, explicit optima exist, such as \omega_b \approx 2 / (1 + \pi / N ) for an N \times N grid.^[19]^[17]^[21] Convergence of all these methods requires \rho(G) < 1, a necessary and sufficient condition independent of the initial guess. For the 1D Poisson equation discretized on a uniform grid with spacing h = 1/(n+1), the tridiagonal matrix A = \frac{1}{h^2} \operatorname{tridiag}(-1, 2, -1) is symmetric positive definite and diagonally dominant. Here, Jacobi's spectral radius is \rho(G_J) = \cos(\pi h) \approx 1 - \frac{1}{2} (\pi h)^2, Gauss-Seidel's is [\rho(G_J)]^2 \approx 1 - (\pi h)^2, and optimal SOR achieves \rho(G_{\omega_b}) = \omega_b - 1 \approx 1 - 2 (\pi h), demonstrating SOR's superior rate, requiring O(n) iterations compared to O(n²) for Gauss-Seidel, thus far fewer for large n.^[17]^[22]^[23] Comparisons reveal that Jacobi requires the fewest operations per iteration due to parallelism but often needs more iterations; Gauss-Seidel halves the iteration count for many sparse matrices at the cost of sequential computation; SOR can further reduce iterations significantly (often by a factor proportional to √n in model problems) with proper \omega, though estimating the optimum can be challenging without prior spectral knowledge. All converge under diagonal dominance, but SOR extends reliably to positive definite systems where Jacobi may falter.^[17]^[24]^[21]

Krylov Subspace Methods

Krylov subspace methods are a class of non-stationary iterative techniques designed to solve large-scale sparse linear systems Ax = b, where A is an n \times n matrix, by projecting the problem onto successively larger Krylov subspaces generated from the initial residual. The m-th Krylov subspace is defined as K_m(A, r_0) = \span\{r_0, A r_0, \dots, A^{m-1} r_0\}, where r_0 = b - A x_0 is the initial residual corresponding to an initial guess x_0.^[25] These methods build approximations x_m = x_0 + z_m with z_m \in K_m(A, r_0) that minimize some measure of the residual or error in a subspace of dimension m, often through Petrov-Galerkin conditions that enforce orthogonality between the residual and a suitable subspace, such as A K_m(A, r_0).^[26] The conjugate gradient (CG) method is a prominent Krylov subspace algorithm specifically for symmetric positive definite matrices A. It generates a sequence of residuals r_k that are mutually orthogonal and search directions p_k that are A-conjugate, meaning p_i^T A p_j = 0 for i \neq j, ensuring short-recurrence updates and exact solution in at most n steps in exact arithmetic.^[27] At each iteration k, the approximation x_k minimizes the A-norm of the error over K_k(A, r_0), leveraging the orthogonality properties to compute the next residual as r_{k+1} = r_k + \alpha_k A p_k with scalar \alpha_k chosen to maintain residual orthogonality.^[27] For nonsymmetric matrices, the generalized minimal residual (GMRES) method minimizes the Euclidean norm of the residual \|r_m\|_2 over the Krylov subspace at each step. It employs the Arnoldi process to construct an orthonormal basis V_m for K_m(A, r_0), yielding a Hessenberg matrix H_m such that A V_m = V_{m+1} H_m, and solves a least-squares problem \min \| \beta e_1 - H_m y \|_2 for the update coefficients, where \beta = \|r_0\|_2 and e_1 is the first unit vector.^[26] To manage storage, GMRES is often restarted after m steps (e.g., GMRES(m)), discarding the basis and continuing from the current approximation, though this may slow convergence.^[26] The biconjugate gradient stabilized (BiCGSTAB) method addresses instabilities in the basic biconjugate gradient (BiCG) for nonsymmetric systems by introducing a polynomial stabilization that smooths convergence without requiring complex arithmetic. It alternates between BiCG-like steps and a double GMRES(2)-like minimization over a two-dimensional subspace to reduce residual norms, producing residuals that are not strictly minimized but exhibit faster and more robust decrease compared to BiCG.^[28] These methods typically require storage of O(m) vectors for the subspace basis or directions, with each iteration involving O(m) matrix-vector products and orthogonalizations, leading to a computational cost of O(m^2) per iteration for building the full subspace up to dimension m. Preconditioners can be integrated to cluster eigenvalues and enhance convergence by effectively reducing the condition number.^[25]

Preconditioning Techniques

Preconditioning transforms the linear system Ax = b into an equivalent form that accelerates the convergence of iterative solvers by introducing a matrix M \approx A that is easier to invert than A. The goal is to solve M^{-1}Ax = M^{-1}b (left preconditioning) or AM^{-1}y = b with x = M^{-1}y (right preconditioning), thereby improving the spectral properties of the effective coefficient matrix. This approach reduces the spectrum's spread, leading to fewer iterations for convergence.^[5] In left preconditioning, the iteration matrix becomes M^{-1}A for stationary methods, and residuals are minimized in the M-norm, which clusters eigenvalues near unity for faster convergence. Right preconditioning yields the iteration matrix AM^{-1}, preserving the original Euclidean residual while allowing flexible, iteration-dependent M in non-symmetric solvers, though it may complicate symmetry preservation. The distinction affects residual computation and eigenvalue distribution; left preconditioning suits symmetric positive definite systems, while right enables variable preconditioning without altering residuals directly.^[5] Diagonal preconditioners use M as the diagonal of A, offering simple row/column scaling to enhance diagonal dominance with minimal computational overhead, particularly effective for mildly ill-conditioned systems. Symmetric successive over-relaxation (SSOR) preconditioners approximate the inverse via forward and backward Gauss-Seidel sweeps with relaxation parameter \omega < 2, forming M = (D - \omega L)D^{-1}(D - \omega U) where A = D - L - U^T, providing better approximation for symmetric matrices at the cost of sequential operations.^[29] Incomplete LU (ILU) preconditioners compute a sparse M = LU by dropping small elements during Gaussian elimination on A, controlling fill-in to maintain efficiency for sparse, nonsymmetric systems. The ILU(0) variant matches the nonzero pattern of A, while higher-order ILU(k) allows limited fill up to level k, as introduced for M-matrices. Threshold-based ILUT drops elements below a tolerance, balancing accuracy and sparsity. Multigrid preconditioners apply a V-cycle algorithm to elliptic partial differential equation discretizations, involving pre- and post-smoothing (e.g., Jacobi or Gauss-Seidel) on fine grids to damp high-frequency errors, followed by restriction to coarser grids for low-frequency correction via exact solves or recursion, and prolongation back to finer levels. This hierarchical approach achieves convergence rates independent of problem size for well-structured operators. Selecting a preconditioner requires balancing the quality of approximation to A against the solve cost for M z = r and induced fill-in, which can degrade sparsity; for instance, aggressive ILU reduces iterations but increases setup time. Well-chosen preconditioners substantially lower the effective condition number, such that \kappa(M^{-1}A) \ll \kappa(A), often reducing iterations by orders of magnitude. These techniques are commonly paired with Krylov subspace methods for robust performance on large-scale problems.^[29]

Origins of Iterative Methods

The origins of iterative methods trace back to ancient civilizations, where numerical approximations for basic computations laid the groundwork for successive refinements. In ancient Babylon around 1800 BCE, early iterative techniques were employed to compute square roots, as evidenced by cuneiform tablets demonstrating approximations like that for √2 on tablet YBC 7289, which used a process akin to repeated averaging of an initial guess and the target value divided by the guess.^[30] These methods represented one of the earliest known applications of iteration for solving nonlinear equations, predating formal mathematics by millennia and influencing later numerical practices in Greece and India.^[30] In the 19th century, iterative approaches gained prominence in the context of solving linear systems arising from scientific computations, particularly in astronomy and geodesy. Carl Friedrich Gauss's 1809 work on the method of least squares, detailed in Theoria Motus Corporum Coelestium, introduced normal equations for overdetermined systems, which initially favored direct elimination but inspired iterative solutions for larger matrices due to computational limitations of the era.^[30] Gauss further advocated iterative methods explicitly in a 1823 letter, highlighting their efficiency for large-scale problems over Gaussian elimination, while his investigations into continued fractions around the same period embodied iterative refinement in approximating irrational numbers.^[30] Building on this foundation, Carl Gustav Jacob Jacobi developed the Jacobi iterative method in 1845, specifically for solving systems of linear equations derived from least squares problems in potential theory and astronomy.^[30] This approach involved decomposing the system matrix and updating variables simultaneously, marking a key advancement in stationary iteration for diagonally dominant matrices. Later, in 1874, Philipp Ludwig von Seidel, a student of Jacobi, refined this into the Gauss-Seidel method, incorporating successive substitutions to accelerate convergence for similar applications in potential theory and stellar analysis.^[30] These methods emerged from the need to handle equations modeling physical potentials, such as in electrostatics and gravitation, where direct solvers were impractical. Early 20th-century developments extended iterative techniques to partial differential equations (PDEs). In 1910, Lewis Fry Richardson introduced an iterative scheme for solving PDEs via explicit time-stepping in finite differences, applied to problems like heat conduction and fluid dynamics, serving as a precursor to relaxation methods in numerical weather prediction.^[19] This non-stationary iteration used dynamic parameter adjustments to refine solutions over "time steps," addressing boundary value problems in engineering and geophysics. A pivotal milestone came in the 1940s with Richard Vynne Southwell's systematic relaxation methods, formalized in his 1940 book Relaxation Methods in Engineering Science, which applied iterative constraint relaxation to solve elliptic PDEs in structural analysis and heat transfer. Southwell's approach, developed during wartime engineering challenges at the Royal Aircraft Establishment, emphasized manual iteration on grids for practical problems like aircraft stress distribution, popularizing relaxation as a versatile tool before widespread computing. The transition to computer-era iterative methods accelerated post-World War II, driven by the need to solve massive linear systems in emerging fields like quantum mechanics and weather prediction. In quantum mechanics, large eigenvalue problems from Hartree-Fock approximations for molecular orbitals required iterative solvers for matrices exceeding hand-calculation scales, as seen in early electronic structure computations at Los Alamos.^[30] Similarly, numerical weather prediction efforts, building on Richardson's ideas, utilized iterative techniques for discretizing atmospheric PDEs on ENIAC in 1950, handling systems with thousands of variables from geophysical data.^[31] This shift marked iterative methods' evolution from manual aids to essential computational paradigms. Successive approximation approaches emerged as a related thread, refining these early techniques for broader applicability.

Successive Approximation Approaches

Successive approximation methods provide a foundational iterative framework for solving nonlinear problems, including ordinary differential equations (ODEs) and systems of nonlinear equations, by generating a sequence of increasingly accurate estimates that converge to the solution under suitable conditions. These approaches build on the idea of refining an initial guess through repeated applications of an operator, often leveraging contraction mappings to ensure convergence. In the context of ODEs, the method traces back to Émile Picard's development in the late 19th century, which was later refined to establish existence and uniqueness theorems. A seminal contribution is the Picard-Lindelöf theorem, which guarantees the existence and uniqueness of solutions to initial value problems for first-order ODEs under Lipschitz continuity assumptions on the right-hand side function. The theorem employs successive approximations defined by the integral equation x_{n+1}(t) = x_0 + \int_{t_0}^t f(s, x_n(s)) \, ds, starting from an initial function x_0(t), where f is continuous and Lipschitz in its second argument. This iterative process constructs a sequence that converges uniformly to the unique solution on a suitable interval, provided the Lipschitz constant and interval length satisfy certain bounds to ensure the mapping is a contraction. The approach, originally introduced by Picard in 1890 and strengthened by Lindelöf's 1907 analysis for the Lipschitz case, forms the basis for proving local existence in nonlinear dynamical systems.^[32] Beyond ODEs, successive approximations extend to solving nonlinear equations through fixed-point theorems in Banach spaces, particularly the Banach contraction principle. This theorem states that if a complete metric space is mapped by a contraction operator (with Lipschitz constant less than 1), then there exists a unique fixed point, and the iterates of successive approximations converge to it from any initial point. Applied to nonlinear equations F(x) = 0, one reformulates as x = G(x) where G is a contraction, enabling iterative solutions in abstract settings like integral equations or functional analysis problems. Stefan Banach established this result in 1922, providing a powerful tool for existence and uniqueness in infinite-dimensional spaces without relying on explicit inverses.^[33] The Newton-Raphson method exemplifies successive approximation for root-finding in nonlinear equations, achieving quadratic convergence through local linearization. Given a differentiable function f with f'(x^*) \neq 0 at the root x^*, the iteration x_{n+1} = x_n - \frac{f(x_n)}{f'(x_n)} updates the approximation by solving the linear tangent equation at each step. Under sufficient smoothness (twice continuously differentiable f) and proximity to x^*, the error satisfies |e_{n+1}| \approx C |e_n|^2 for some constant C, ensuring rapid convergence once initiated near the root; this quadratic rate was rigorously analyzed in early numerical analysis texts building on the method's origins in the 17th century.^[34] In optimization and control, successive approximations appear in policy iteration for dynamic programming, where value functions are updated iteratively to solve Bellman equations. Developed by Richard Bellman in the 1950s, this framework addresses Markov decision processes by alternating policy evaluation (solving the linear system for the current policy's value) and policy improvement (greedily selecting actions based on the value). Ronald Howard formalized policy iteration in 1960, proving finite convergence to the optimal policy for finite-state problems by showing each iteration strictly improves the value function until optimality. This method applies successive approximations to the value function updates, enabling solutions to complex sequential decision problems in operations research.^[35] Twentieth-century extensions of successive approximations include quasi-Newton methods, which approximate the Jacobian or Hessian without full recomputation to enhance efficiency for large-scale nonlinear systems. Broyden's method, introduced in 1965, updates an approximate Jacobian B_n using secant conditions from function and derivative differences, yielding a rank-one modification that maintains positive definiteness under certain safeguards. This derivative-free update achieves superlinear convergence for systems where exact derivatives are costly, extending Newton's approach to high dimensions while reducing computational overhead.^[36] Despite their strengths, successive approximation methods exhibit limitations, particularly sensitivity to the initial guess and requirements for function smoothness. Poor initial approximations can lead to divergence or slow convergence, as seen in Newton-Raphson where basins of attraction may exclude distant starting points, necessitating globalization techniques like line searches. Additionally, the methods assume Lipschitz continuity or differentiability, failing for nonsmooth functions where contractions do not hold, potentially requiring regularization or alternative approaches.^[37]

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The History of Numerical Weather Prediction - NOAA
Oct 31, 2023 · The first one-day, nonlinear weather prediction was made in April, 1950. Its completion required the round-the-clock services of the modelers, ...
[32]
New applications of Picardʼs successive approximations
The iterative method of successive approximations, originally introduced by Émile Picard in 1890, is a basic tool for proving the existence of solutions of ...Missing: original | Show results with:original
[33]
The Banach Fixed Point Theorem: selected topics from its hundred ...
The Banach Fixed Point Theorem was not the ﬁrst theorem connected with ﬁxed points. One of the ﬁrst theorems were formulated by Henri Poincaré in 1886. In 1909 ...
[34]
[PDF] Quadratic Convergence of Newton's Method - NYU Computer Science
The quadratic convergence rate of Newton's Method is not given in A&G, except as Exercise 3.9. However, it's not so obvious how to derive it, even though.Missing: source | Show results with:source
[35]
[PDF] Dynamic Programming and Markov Processes - Gwern
The policy-iteration method that will be described will find the optimal policy in a small numberof iterations. It is composed of two parts, the value- ...Missing: paper | Show results with:paper
[36]
[PDF] A Class of Methods for Solving Nonlinear Simultaneous Equations ...
This paper discusses certain modificatioins to Newton's method designed to re- duce the number of function evaluations required. Results of various ...
[37]
The Newton-Raphson Method: A Detailed Analysis - IJRASET
Rate of Convergence The Newton-Raphson method is renowned for its quadratic convergence when the initial guess is sufficiently close to the true root.Missing: source | Show results with:source