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sequential quadratic programming method  (MathWorks Inc)
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sequential quadratic programming sqp procedure  (MathWorks Inc)
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MathWorks Inc sequential quadratic programming sqp procedure
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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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integer sequential quadratic programming mi sqp toolbox  (MathWorks Inc)
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MathWorks Inc integer sequential quadratic programming mi sqp toolbox
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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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sequential quadratic programming (sqp) algorithm  (ESTECO s p a)
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ESTECO s p a sequential quadratic programming (sqp) algorithm
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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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nonlinear economic model predictive control for si engines based on sequential quadratic programming  (PRUCKA ENGINEERING INC)
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PRUCKA ENGINEERING INC nonlinear economic model predictive control for si engines based on sequential quadratic programming
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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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leastsquaresfit function  (Parametric Technology Corporation)
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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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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hybrid ep and sqp  (Hasegawa Co Ltd)
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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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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sqp  (TheWell Bioscience Inc)
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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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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hybrid computing structure of the ga-sqp  (Nonlinear Dynamics)
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Nonlinear Dynamics hybrid computing structure of the ga-sqp
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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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code for sqp solver  (SAS institute)
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SAS institute code for sqp solver
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; <t>SQP—</t> Sequential Quadratic Programming; a.u.—arbitrary units.
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Image Search Results


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: