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matlab optimization toolbox  (MathWorks Inc)


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    MathWorks Inc matlab optimization toolbox
    Matlab Optimization Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 3333 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/least-square+algorithm+matlab/Optimization+Toolbox/pm19191287-78-27-30
    Average 96 stars, based on 3333 article reviews
    matlab optimization toolbox - by Bioz Stars, 2026-11
    96/100 stars

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    Article Title: Refined modelling of hypervelocity impact crater size predictions
    Article Snippet: The parameters were identified through non-linear least-squares optimization using the MATLAB function lsqcurvefit.

    Article Title: Solid-state NMR dipolar recoupling in the presence of large chemical shielding anisotropies by quaternion-based effective Hamiltonian optimal control.
    Article Snippet: Dipolar recoupling is a key element in magic-angle-spinning (MAS) solid-state NMR spectroscopy with reintroduced dipole–dipole coupling interactions providing information about internuclear distances and enabling transfer of polarization between spins in resolution-enhancing multiple-dimensional experiments.. Such methods may be challenged in many important applications by the presence of large anisotropic nuclear spin interactions such as chemical shielding anisotropy.. In this paper, we address this challenge by presenting quaternion-based optimal control.

    Article Title: Sparse time-delay and amplitude estimation of multipath arrivals in ocean acoustics.
    Article Snippet: We estimate multipath arrival time delays and amplitudes with high resolution for accurate characterization of ocean acoustic channels.. Although matched filtering (MF) is robust and widely used, its output represents a superposition of contributions from multiple arrivals, which can bias delay and amplitude estimates.. More advanced high-resolution (HR) methods exist but are usually computationally expensive.

    Article Title: Resistance Profile of Listeria monocytogenes and Escherichia coli to High Power Ultrasound in Juice Matrices.
    Article Snippet: Regression analyses were performed to obtain the parameter estimation of the primary and secondary model using Matlab Version 6.1 (MathWorks, MA, USA) while the parameter optimization was performed with the optimization function of Isqnonlin of the Matlab Optimisation Toolbox.

    Article Title: Optimization of the Volumetric Overall Mass Transfer Coefficient in an L-Shaped Pulsed Sieve Plate Extraction Column
    Article Snippet: Nonlinear regression and optimization were performed in MATLAB R2022b using the Optimization Toolbox (Version 9.4) and the lsqnonlin solver.

    Article Title: Trade-off between flux efficiency and metabolic control shapes enzyme allocation.
    Article Snippet: As the whole procedure computes pathway flux J from kcat ;KS;KP;K;E; Sin1⁄2 ; 1⁄2Sout , the pathway flux J can also be written as a function of these parameters and concentrations: J 1⁄4 Jðkcat ;KS;KP;K ;E; Sin1⁄2 ; 1⁄2Sout Þ Optimal enzyme allocation profile maximizing flux efficiency under a given parameter set was computed using the MATLAB constrained optimization solver fmincon[] to maximize the pathway flux with the upper bound of total enzyme abundance set to 1: max E Jðkcat ;KS;KP;K ;E; Sin1⁄2 ; 1⁄2Sout Þ s:t: 0 1⁄2Ei 1Pn i1⁄41 Ei1⁄2 1

    Article Title: Network-Irreducible Multiparty Entanglement in Quantum Matter.
    Article Snippet: [27] MOSEK Optimization Toolbox for MATLAB 10.2.17, MOSEK ApS (2024), https://docs.mosek.com/10.2/toolbo x/index.html.

    Article Title: Protocol for thermodynamically optimal construction and analysis of metabolic networks using ThermOptCOBRA.
    Article Snippet: Other MATLAB toolboxes to be installed are Optimization Toolbox, Statistics and Machine Learning Toolbox, and Parallel Computing Toolbox.



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    MathWorks Inc nonnegative linear least-squares spectral unmixing algorithm matlab isqnonneg
    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral <t>unmixing</t> algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.
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    MathWorks Inc least-squares algorithms built into matlab r2022b
    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral <t>unmixing</t> algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.
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    Image Search Results


    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

    Journal: Journal of Biomedical Optics

    Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

    doi: 10.1117/1.JBO.29.1.016008

    Figure Lengend Snippet: SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

    Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

    Techniques: Imaging

    Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

    Journal: Journal of Biomedical Optics

    Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

    doi: 10.1117/1.JBO.29.1.016008

    Figure Lengend Snippet: Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

    Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

    Techniques: