Working on lipschitz function and contraction map

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SUMMARY

The discussion centers on demonstrating that for a function f: R² → R² defined as f(x) = , where f₁ and f₂ are differentiable functions, the 2-norm of the difference f(x) - f(y) is bounded by a specific expression involving constants K₁ and K₂. It is established that if the 2-norm of the gradients of f₁ and f₂ are less than or equal to K₁ and K₂ respectively, then the inequality ||f(x) - f(y)||₂ ≤ √(K₁² + K₂²) * ||x - y||₂ holds for all x, y in R². This conclusion is derived using the properties of the 2-norm and the results from related mathematical discussions.

PREREQUISITES
  • Understanding of differentiable functions in R²
  • Familiarity with the 2-norm and its properties
  • Knowledge of gradient concepts in multivariable calculus
  • Basic principles of contraction mappings
NEXT STEPS
  • Study the properties of the 2-norm in vector spaces
  • Explore the implications of the Mean Value Theorem in multivariable calculus
  • Learn about contraction mappings and their applications in analysis
  • Review the results from the referenced discussion on converging maps
USEFUL FOR

Mathematicians, students of calculus, and anyone interested in the analysis of functions and contraction mappings in multivariable contexts.

simo1
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if you given a function f from R^2 to R^2 f(x)=<f_1(x),f_2(x)>, x in R^2

with f_1 and f_2 from R^2 to R being differentiable on R. if there is contants K_1 and K_2 greater than or equal to 0 so the 2-norm of (gradient f_1(x)) is less than or equal to K_1 and 2-norm of (gradient f_2(x)) is less than or equal to K_2 for x in R^2.
show that the 2-norm of (f(x)-f(y)) is less than or equal to [square root of (k_1^2 -k_2^2)] multipy by 2-norm of (x-y) for all x and y in R^2

can i get hints on how to start
 
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simo said:
if you given a function f from R^2 to R^2 f(x)=<f_1(x),f_2(x)>, x in R^2

with f_1 and f_2 from R^2 to R being differentiable on R. if there is contants K_1 and K_2 greater than or equal to 0 so the 2-norm of (gradient f_1(x)) is less than or equal to K_1 and 2-norm of (gradient f_2(x)) is less than or equal to K_2 for x in R^2.
show that the 2-norm of (f(x)-f(y)) is less than or equal to [square root of (k_1^2 – k_2^2)] multipy by 2-norm of (x-y) for all x and y in R^2 (That – should be a +.)

can i get hints on how to start
Hi simo, and welcome to MHB! By the definition of the 2-norm, $\|f(x) - f(y)\|_2^2 = |f_1(x) - f_1(y)|^2 + |f_2(x) - f_2(y)|^2.$ Now use the result from http://mathhelpboards.com/analysis-50/converging-maps-9673.html (applied to the functions $f_1$ and $f_2$) to deduce that $\|f(x) - f(y)\|_2^2 \leqslant (K_1^2 + K_2^2)\|x-y\|^2.$ Then take the square root of both sides.
 

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