Picard–Lindelöf Iteration When h(x,y) and h_y(x,y) Are Bounded and Continuous Ev
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Differential equation Material
This GeoGebra example dynamically illustrates the Picard–Lindelöf iteration process for an ordinary differential equation, demonstrating that when the function h(x,y) and its partial derivative h_y(x,y) are bounded and continuous on the entire plane, the solution to the initial value problem exists not only locally but can be extended to the whole domain. The core mathematical concepts include the existence and uniqueness theorem for ODEs, Picard iteration sequences, uniform convergence, and the Lipschitz condition. The construction uses GeoGebra's Textfield and InputBox objects (InputBox1, InputBox2, InputBox3) to let users input initial conditions, the iteration number n, and the function h; the Integral command computes the integral expressions in each iteration step, generating a sequence of function objects f_1 through f_17 and corresponding g_1 through g_17 that progressively approximate the exact solution. This is suitable for university-level mathematics students studying differential equations, numerical analysis, or qualitative theory of ODEs, and also serves as a classroom demonstration for instructors to visualize the constructive proof of the existence and uniqueness theorem.
GeoGebra Commands & Parameters
Textfield(a) → InputBox1Textfield(b) → InputBox2Integral(h(x, a), x) → f_{1}Integral(h(x, g_{1}), x) → f_{2}Integral(h(x, g_{2})) → f_{3}Integral(h(x, g_{3}), x) → f_{4}Textfield(h) → InputBox3Integral(h(x, g_{4}), x) → f_{5}Integral(h(x, g_{5}), x) → f_{6}Integral(h(x, g_{6}), x) → f_{7}Integral(h(x, g_{7}), x) → f_{8}Integral(h(x, g_{8}), x) → f_{9}Integral(h(x, g_{9}), x) → f_{10}Integral(h(x, g_{10}), x) → f_{11}Integral(h(x, g_{11}), x) → f_{12}Integral(h(x, g_{12}), x) → f_{13}Integral(h(x, g_{13}), x) → f_{14}Integral(h(x, g_{14}), x) → f_{15}Integral(h(x, g_{15}), x) → f_{16}Integral(h(x, g_{16}), x) → f_{17}Integral(h(x, g_{17}), x) → f_{18}Integral(h(x, g_{18}), x) → f_{19}Integral(h(x, g_{19}), x) → f_{20}Integral(h(x, g_{20}), x) → f_{21}Integral(h(x, g_{21}), x) → f_{22}Integral(h(x, g_{22}), x) → f_{23}Integral(h(x, g_{23}), x) → f_{24}Integral(h(x, g_{24}), x) → f_{25}Integral(h(x, g_{25}), x) → f_{26}Integral(h(x, g_{26}), x) → f_{27}Integral(h(x, g_{27}), x) → f_{28}Integral(h(x, g_{28}), x) → f_{29}Integral(h(x, g_{29}), x) → f_{30}Textfield(f) → InputBox4
AI Drawing Prompt Reference
The graph illustrates the Picard-Lindelöf iteration process for solving ordinary differential equations. Users input initial conditions, iteration number, and function h. The graph displays the sequence of iteration functions f1 to f17 progressively approaching the exact solution. It includes initial points, iteration curves, and the approximating solution curve, demonstrating existence and uniqueness. The construction order starts with initial conditions, then computes integrals to obtain iteration functions, and finally approaches the solution. The graph visually shows the convergence of the Picard iteration sequence, suitable for differential equations teaching.
The prompt above was compiled from the original drawing commands and is for reference only.