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constrained non-linear minimization (fmincon) method with sequential quadratic programming (sqp) algorithm  (MathWorks Inc)


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    MathWorks Inc constrained non-linear minimization (fmincon) method with sequential quadratic programming (sqp) algorithm
    Constrained Non Linear Minimization (Fmincon) Method With Sequential Quadratic Programming (Sqp) Algorithm, 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+(sqp)+method/pm33559086-113-8-13
    Average 90 stars, based on 1 article reviews
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    Article Title: Use of exergy efficiency for the optimization of LNG processes with NGL extraction
    Article Snippet: Thus, with the two suggested performance parameters ( and ), optimization studies were conducted with the problem formulation provided by Eqs. (15)-(17). minm `"m% = Obj "m% ∨ Objr"m% subject to Δ t, "m% ≥ 3 a = {HE-1, 2, 3,4} Δ / ,|"m% ≥ 0 } = {W04, C06} 1 ≤ "m% ≤ 4 = {K- 1, 2,3,4,5,6} - "m% ≤ 1 mol % ∑ - - "m% ≤ 0.1 mol % = {i- C , n- C ... ,m- Xylene} ∑ - - "m% ≤ 10 ppm C = {Benzene, Toluene,m- Xylene} m ≤ m ≤ m (15) where Obj = "m% (16) Objr = "m% (17) Optimization was performed using a local solver based on a sequential quadratic programming (SQP) algorithm in Matlab.

    Article Title: A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems
    Article Snippet: Grey Wolf Optimizer (GWO) tends to converge prematurely when dealing with multimodal problems.. Using the benefits of hybridizing algorithm to boost the performance of GWO is a recent trend.. Therefore, a novel improved GWO called collaboration-based Hybrid GWO-SCA optimizer (cHGWOSCA) is developed.

    Article Title: Tracking Chain Populations and Branching Structure during Polyethylene Deconstruction Processes
    Article Snippet: The properties of the populations can be written as: Mn = μ exp (− σ2 2 ) (S31) Mw = μ exp ( σ2 2 ) (S32) Mz = μ exp ( 3σ2 2 ) (S33) Ð = exp (σ2) (S34) To achieve a meaningful fit to molar mass distributions, a constrained minimization is performed such that the sum-of-squared residuals is minimized subject to the following constraints: 1. all parameters are positive, 2. the populations sum to the whole measured distribution, i.e., ∫ G(log M)d(log M) ∞ −∞ = ∫ dw d(log M) d(log M) ∞ −∞ ≡ 1 (S35) ∴ ∑ mi N i=1 = ln 10 (S36) 3. the peak center is within a small margin of the guess, specifically: 10−λ ⋅ μi,guess ≤ μi ≤ 10 λ ⋅ μi,guess (S37) 4. peaks are of narrow dispersity, i.e., Ð < Ðmax (S38) ∴ σi ≤ √ln Ðmax (S39) The constrained minimization problem defined by (S30, S35-S39) is solved using a sequential quadratic programming (SQP) algorithm via MATLAB’s fmincon function.

    Article Title: Failure analysis of brazed sandwich structures with square honeycomb-corrugation hybrid cores under three-point bending
    Article Snippet: Expressed in nondimensional form, it written: W = W ρsL2 (25) The non-dimensional geometric parameters and material parameter can be written as: ρf = ρf∕ρs; tf = tf∕c; t = t∕c; c = c∕L (26) a = a∕L; d = c + tf ; λ = t∕ cos θ (27) σ = σ ∕σ ;E = E ∕E ; σ = σ ∕σ (28) c c y h s fw fw y t d b e t o d o a d b It follows that: ∑ cy 23 = cy ∑ 23 ∕σy = ( λ 2 sin(2θ) + (1 − λ)⌢E (1 + νf )(sin(2θ)) ) σc (29) ∑ cy 33 = cy ∑ 33 ∕σy = ( λ sin2 θ + (1 − λ)⌢E sin2 θ ) σc (30) The non-dimensional W and F may be presented as: W = 2tf c + cρc (Sandwich mass) (31) F FY = 4tf (tf + 1)c 2∕(1 − a2) (Face yielding) (32) F FW = 4tf (tf + 1)c 2σFW ∕(1 − a 2) (Face wrinkling) (33) FCS = 4t 2 f c 2∕(1 − a2) + 2c ∑ cy 23 (Core shear) (34) F IND = 2tf c √ ∑ cy 33 + a ∑ cy 33 (Core shear) (35) Load based minimum weight design is performed by using a sequential quadratic programming (SQP) algorithm coded in MATLAB, based on the constraints that none of the failure modes in (32)–(35) occurs.

    Article Title: Experimental speed-of-sound data and a fundamental equation of state for normal hydrogen optimized for flow measurements
    Article Snippet: The branch and bound algorithm of Kuipers [38] was applied to solve the integer problem and the relaxed sub-problems were solved using a sequential quadratic programming algorithm (SQP) available in MATLAB [37,39].

    Article Title: Optimal Control of Colloidal Trajectories in Inertial Microfluidics Using the Saffman Effect
    Article Snippet: With this, we optimize the discretized cost functional J [ f ctl ( t ) , T ] while using a sequential quadratic programming (sqp) algorithm [ ] provided by the package fmincon from MathWorks’ software matlab (Release R2019b, https://mathworks.com/help/optim/ug/constrained-nonlinear-optimization-algorithms.html ).

    Article Title: Spin-state-controlled chemi-ionization reactions between metastable helium atoms and ground-state lithium atoms.
    Article Snippet: The spectrum is fitted to the experimental data in a constrained least-squares manner using a sequential quadratic programming (SQP) algorithm provided by MATLAB.

    Article Title: Altered mechanical properties of actin fibers due to breast cancer invasion: parameter identification based on micropipette aspiration and multiscale tensegrity modeling.
    Article Snippet: The biophysical properties of cells change with cancer invasion to fulfill their metastatic behavior.. Cell softening induced by cancer is highly associated with alterations in cytoskeleton fibers.. Changes in the mechanical properties of cytoskeletal fibers have not been quantified due to technical limitations.



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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: