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A discrete Newton algorithm for minimizing a function of many variables
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Conjugate Direction Methods in Optimization
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CGS algorithms for unconstrained minimization of functions
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Convergence of Newton-like methods for solving systems of nonlinear equations
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Newton-type methods for unconstrained and linearly constrained optimization
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Truncated-Newton algorithms for large-scale unconstrained optimization
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Solution of linear systems of equations: Iterative methods
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A GENERALIZED CONJUGATE GRADIENT METHOD FOR THE NUMERICAL SOLUTION OF ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS
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Estimates for Some Computational Techniques in Linear Algebra
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On the Estimation of Sparse Jacobian Matrices
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A Matrix Modification Method for Calculating Approximate Solutions to Systems of Linear Equations
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The Conjugate Gradient Method for Linear and Nonlinear Operator Equations
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Direct Methods for Solving Symmetric Indefinite Systems of Linear Equations
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Some Efficient Algorithms for Solving Systems of Nonlinear Equations
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Solution of Sparse Indefinite Systems of Linear Equations
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A Modified Newton’s Method for Unconstrained Minimization
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On the Estimation of Sparse Hessian Matrices
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Iterative Solution of Nonlinear Equations in Several Variables
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Conjugate-Gradient Methods for Large-Scale Nonlinear Optimization.
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QDN: a variable storage algorithm for unconstrained optimization
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Iterative Solution of Nonlinear Equations in Several Variables
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Numerical Methods for Unconstrained Optimization
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An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
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Solution of systems of linear equations by minimized iterations
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Methods of conjugate gradients for solving linear systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Optimization Methods in Computational Chemistry
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A powerful truncated Newton method for potential energy minimization
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An increasing‐angle property of the conjugate gradient method and the implementation of large‐scale minimization algorithms with line searches
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Modified Cholesky Factorizations of the Hessian Matrix for Minimizing Nonlinear Functions
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Large Scale Unconstrained Optimization
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Large Scale Unconstrained Optimization
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A Class of Methods Combining L-BFGS and Truncated Newton
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Towards a Discrete Newton Method with Memory for Large-Scale Optimization
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Large-Scale Unconstrained Optimization
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Techniques for Large Sparae Systems Arising from Continuation Methods
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Solution of Large Linear Systems of Equations by Conjugate Gradient Type Methods
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An Overview of Unconstrained Optimization
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A truncated Newton optimization algorithm in meteorology applications with analytic Hessian/vector products
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On the limited memory BFGS method for large scale optimization
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Block truncated-Newton methods for parallel optimization
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Truncated-Newton algorithms for large-scale unconstrained optimization
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Numerical experiments with partially separable optimization problems
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An incomplete Hessian Newton minimization method and its application in a chemical database problem
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Tracing post-limit-point paths with incomplete or without factorization of the stiffness matrix
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Iterative methods for nonlinear operator equations
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Efficient tridiagonal preconditioner for the matrix-free truncated Newton method
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Symbiosis between linear algebra and optimization
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Theory of algorithms for unconstrained optimization
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Enriched Methods for Large-Scale Unconstrained Optimization
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The Adjoint Newton Algorithm for Large-Scale Unconstrained Optimization in Meteorology Applications
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Approaches to robust pole assignment
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Numerical techniques in nonparametric estimation
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Avoiding Modified Matrix Factorizations in Newton-like Methods
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NON-LINEAR OPTIMIZATION AND LARGE-SCALE PROBLEMS
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A new class of quasi-Newton updating formulas for unconstrained optimization
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Dynamic scaling based preconditioning for truncated Newton methods in large scale unconstrained optimization
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A matrix-free line-search algorithm for nonconvex optimization
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A user's guide to nonlinear optimization algorithms
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A user's guide to nonlinear optimization algorithms
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Robust Regression Computation Using Iteratively Reweighted Least Squares
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Newton-Type Minimization via the Lanczos Method
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A Local Convergence Theory for Combined Inexact-Newton/Finite-Difference Projection Methods
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A Numerical Study of the Limited Memory BFGS Method and the Truncated-Newton Method for Large Scale Optimization
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A General-Purpose Parallel Algorithm for Unconstrained Optimization
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Convergence Theory of Nonlinear Newton–Krylov Algorithms
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Nonlinearly Preconditioned Krylov Subspace Methods for Discrete Newton Algorithms
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Sparse Matrix Methods in Optimization
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Preconditioning of Truncated-Newton Methods
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The Use of Iterative Linear-Equation Solvers in Codes for Large Systems of Stiff IVP s for ODE s
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Hybrid Krylov Methods for Nonlinear Systems of Equations
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Modified Cholesky Factorizations for Sparse Preconditioners
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BFGS with Update Skipping and Varying Memory
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Automatic Preconditioning by Limited Memory Quasi-Newton Updating
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Algorithm 711: BTN: software for parallel unconstrained optimization
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Application of a New Adjoint Newton Algorithm to the 3D ARPS Storm-Scale Model Using Simulated Data
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Second-Order Information in Data Assimilation*
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Computational Strategies for Tracing Post-Limit-Point Paths with Pure Iterative Methods
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Computational Strategies for Tracing Post-Limit-Point Paths with Pure Iterative Methods
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Parallel unconstrained minimization of potential energy in LAMMPS
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Fast Energy Minimization of large Polymers Using Constrained Optimization
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