Prove Minkowski's inequality using Cauchy-Schwarz's

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SUMMARY

This discussion proves Minkowski's inequality, stating that for vectors u and v in R^n, the inequality \|u + v\| ≤ \|u\| + \|v\| holds true using the Cauchy-Schwarz inequality. The proof begins with the definition of the norm and expands the expression \|u + v\|^2 to show that it is less than or equal to (\|u\| + \|v\|)^2. The key equations utilized include the dot product definition and the Cauchy-Schwarz inequality, which establishes the relationship between the dot product and the norms of the vectors.

PREREQUISITES
  • Understanding of vector norms in R^n
  • Familiarity with the dot product of vectors
  • Knowledge of the Cauchy-Schwarz inequality
  • Basic algebraic manipulation skills
NEXT STEPS
  • Study the properties of vector norms in R^n
  • Learn more about the applications of the Cauchy-Schwarz inequality
  • Explore other inequalities in functional analysis
  • Investigate the geometric interpretations of vector operations
USEFUL FOR

Mathematics students, educators, and anyone interested in understanding inequalities in vector spaces, particularly those studying linear algebra or functional analysis.

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Homework Statement


For u and v in R^n prove Minkowski's inequality that \|u + v\| \leq \|u\| + \|v\| using the Cauchy-Schwarz inequality theorem: |u \cdot v| \leq \|u\| \|v\|.

Homework Equations


Dot product: u \cdot v = u_1 v_1 + u_2 v_2 + \cdots + u_n v_n
Norm: \|u \| = \sqrt {u \cdot u}
Cauchy-Schwarz inequality: |u \cdot v| \leq \|u\| \|v\|

The Attempt at a Solution


Using def. of norm: \|u + v\|^2 = \sqrt {(u + v) \cdot (u + v)}^2 = (u + v) \cdot (u + v)
Expand: (u + v) \cdot (u + v) = u \cdot u + 2 (u \cdot v) + v \cdot v
Using the Cauchy-Schwarz inequality: u \cdot u + 2 (u \cdot v) + v \cdot v \leq \|u \|^2 + 2 \|u \| \|v\| + \| v \|^2 = (\|u \| + \| v \|)^2

Therefore, \|u + v\|^2 \leq (\|u \| + \| v \|)^2 \Rightarrow \|u + v\| \leq \|u \| + \| v \|.

Thank-you
 
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