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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
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A quadratic equation is an algebraic expression where a variable is raised to the second power and combined with its first power and a constant; all equated to zero. These equations are frequently used to model relationships involving area, motion, and optimization. The general representation of a quadratic equation iswhere a, b, and c are real values, and a is nonzero to ensure the presence of the squared term.One method for solving a quadratic equation involves rewriting it as a product of...
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Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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Related Experiment Video

Updated: Apr 21, 2026

SIVQ-LCM Protocol for the ArcturusXT Instrument
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Algorithm 937: MINRES-QLP for Symmetric and Hermitian Linear Equations and Least-Squares Problems.

Sou-Cheng T Choi1, Michael A Saunders2

  • 1University of Chicago/Argonne National Laboratory.

ACM Transactions on Mathematical Software. Association for Computing Machinery
|October 21, 2014
PubMed
Summary
This summary is machine-generated.

The MINRES-QLP algorithm and its FORTRAN 90 implementation solve symmetric linear systems and least-squares problems. It provides a stable, minimum-length solution for singular systems, improving upon the original MINRES algorithm.

Keywords:
AlgorithmsKrylov subspace methodLanczos processconjugate-gradient methoddata encapsulationill-posed problemlinear equationsminimum-residual methodpseudoinverse solutionregressionsingular least-squaressparse matrix

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Area of Science:

  • Numerical Analysis
  • Scientific Computing
  • Linear Algebra

Background:

  • The MINRES algorithm is used for solving symmetric or Hermitian linear systems and least-squares problems.
  • The standard MINRES algorithm can face instability and does not provide minimum-length solutions for singular systems.

Purpose of the Study:

  • To introduce the MINRES-QLP algorithm and its FORTRAN 90 implementation.
  • To address limitations of the original MINRES algorithm, specifically regarding singular systems and potential instability.
  • To provide a secure and efficient implementation for solving linear systems and least-squares problems.

Main Methods:

  • Development of the MINRES-QLP algorithm, an enhancement of the MINRES algorithm.
  • Implementation of MINRES-QLP in FORTRAN 90, featuring a design pattern for secure data handling and avoiding reverse communication.
  • Utilizing Matrix Market format for inputting test problems.
  • Providing MATLAB versions for broader accessibility.

Main Results:

  • MINRES-QLP successfully computes the unique minimum-length (pseudoinverse) solution for singular systems.
  • The algorithm overcomes potential instabilities present in the original MINRES.
  • The FORTRAN 90 implementation demonstrates a secure design pattern, enhancing usability.
  • Test programs successfully solve real and complex problems.

Conclusions:

  • MINRES-QLP offers a robust and stable solution for symmetric/Hermitian linear systems and least-squares problems, including singular cases.
  • The FORTRAN 90 implementation provides a secure and efficient tool for scientific computing.
  • The algorithm and its implementations are valuable for researchers and practitioners in numerical analysis and scientific computing.