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gradient-based method using the built-in sequential quadratic programming algorithm for constrained nonlinear optimization  (MathWorks Inc)


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    MathWorks Inc gradient-based method using the built-in sequential quadratic programming algorithm for constrained nonlinear optimization
    Gradient Based Method Using The Built In Sequential Quadratic Programming Algorithm For Constrained Nonlinear Optimization, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/sequential+quadratic+programming+method/pm35364905-162-23-26
    Average 90 stars, based on 1 article reviews
    gradient-based method using the built-in sequential quadratic programming algorithm for constrained nonlinear optimization - by Bioz Stars, 2026-09
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    Article Title: The risk of bias in denoising methods: Examples from neuroimaging
    Article Snippet: Specifically, we use nonlinear optimization (MATLAB Optimization Toolbox’s lsqnonlin.m) to determine the optimal parameters for a double-gamma function (as implemented in SPM’s spm_hrf.m) such that when convolved with a 1-s boxcar, the result best approximates the measurement in a least-squares sense.

    Article Title: ON AN INTEGRATED DYNAMIC CHARACTERIZATION OF VISCOELASTIC MATERIALS BY FRACTIONAL DERIVATIVE AND GHM MODELS
    Article Snippet: The minimization was performed by the use of a hybrid strategy, which combined, in sequence, a genetic algorithm and a nonlinear optimization method, as implemented in the ga and fmincon proprietary MATLAB® routines, respectively.

    Article Title: Many-Stage Optimal Stabilized Runge–Kutta Methods for Hyperbolic Partial Differential Equations
    Article Snippet: This was observed when Matlab’s function for general nonlinear optimization,fmincon failed in finding the optimal solution.

    Article Title: Relative extents, mechanisms, and kinetics of fluoride removal from synthetic groundwater by prevalent sorbents.
    Article Snippet: • Defloridation mechanisms and kinetics of eight sorbents compared in

    Article Title: Optogenetic control of receptors reveals distinct roles for actin- and Cdc42-dependent negative signals in chemotactic signal processing
    Article Snippet: All fitting was performed using nonlinear optimization (fminsearch and fminunc in MATLAB) to maximize the Pearson’s correlation between the two images.

    Article Title: Holistic face recognition is an emergent phenomenon of spatial processing in face-selective regions
    Article Snippet: Model fitting was performed using nonlinear optimization (MATLAB Optimization Toolbox).

    Article Title: Ultrasound characterization of bioinspired functionally graded soft-to-hard composites: Experiment and modeling.
    Article Snippet: For each r, the minimization of 1⁄2v1 a0 was achieved with a gradient-based method using the built-in sequential quadratic programming algorithm for constrained nonlinear optimization from Matlab (The MathWorks Inc., Natick, MA).



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    The environment to traverse ( A ) and the simulation pipeline ( B ). The environment was generated as a linear combination of scaled and translated Gaussian surfaces, as described in Methods and Materials. The simulation procedure entailed refining the path predictions by each algorithm separately using the landscape-dependent cost function. Abbreviations: GA—genetic Algorithm; PSO—Particle Swarm Optimization; SQP— Sequential Quadratic Programming; a.u.—arbitrary units.

    Journal: bioRxiv

    Article Title: Evaluating Evolutionary and Gradient-Based Algorithms for Optimal Pathfinding

    doi: 10.1101/2025.03.16.643541

    Figure Lengend Snippet: The environment to traverse ( A ) and the simulation pipeline ( B ). The environment was generated as a linear combination of scaled and translated Gaussian surfaces, as described in Methods and Materials. The simulation procedure entailed refining the path predictions by each algorithm separately using the landscape-dependent cost function. Abbreviations: GA—genetic Algorithm; PSO—Particle Swarm Optimization; SQP— Sequential Quadratic Programming; a.u.—arbitrary units.

    Article Snippet: The third algorithm used in our study was a quasi-Newton method—the Sequential Quadratic Programming (SQP) procedure (“ fmincon ” function in the MATLAB’s Optimization Toolbox)—described in detail in ( ).

    Techniques: Generated, Refining

    Representative paths calculated by the three evaluated algorithms: ( A ) Genetic Algorithm (GA), ( B ) Particle Swarm Optimization (PSO), and ( C ) Sequential Quadratic Programming (SQP), each illustrating the distinct pathfinding solutions. Abbreviations are the same as in .

    Journal: bioRxiv

    Article Title: Evaluating Evolutionary and Gradient-Based Algorithms for Optimal Pathfinding

    doi: 10.1101/2025.03.16.643541

    Figure Lengend Snippet: Representative paths calculated by the three evaluated algorithms: ( A ) Genetic Algorithm (GA), ( B ) Particle Swarm Optimization (PSO), and ( C ) Sequential Quadratic Programming (SQP), each illustrating the distinct pathfinding solutions. Abbreviations are the same as in .

    Article Snippet: The third algorithm used in our study was a quasi-Newton method—the Sequential Quadratic Programming (SQP) procedure (“ fmincon ” function in the MATLAB’s Optimization Toolbox)—described in detail in ( ).

    Techniques: