Prove 1/n tends to zero using the epsilon definition.
Answer and explanation
Given ε>0, choose an integer N>1/ε. For n≥N, 0<1/n≤1/N<ε.
Credit the order of quantifiers, an explicit N, and a bound for every n≥N.
Subjects
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Given ε>0, choose an integer N>1/ε. For n≥N, 0<1/n≤1/N<ε.
Credit the order of quantifiers, an explicit N, and a bound for every n≥N.
If a_n→L, eventually abs(a_n−L)<1, so the tail is bounded by abs(L)+1. Take the maximum of that bound and the finitely many initial absolute values.
Credit both tail control and the finite initial segment.
There exists one N that works for every ε>0
The needed cutoff generally grows as ε shrinks.
101
The strict inequality requires n>100, not n≥100.
a_n=(−1)^n is bounded by 1 in absolute value, but its even and odd subsequences have limits 1 and −1.
Credit boundedness and a valid nonconvergence argument.
Completeness
Completeness prevents a Cauchy sequence from approaching a missing real-number limit.
For every ε>0 there exists N such that every n≥N satisfies the error bound.
Advanced universityFor every ε>0 there exists N such that every n≥N satisfies the error bound.
Advanced universityThe cutoff N may depend on both ε and the point x.
Advanced universityThe cutoff N may depend on both ε and the point x.
Advanced universityIn R^n, compactness is equivalent to being closed and bounded.
Advanced universityIn R^n, compactness is equivalent to being closed and bounded.
Advanced universityA bound uniform in x proves more than pointwise convergence at a chosen x.
Advanced universityA bound uniform in x proves more than pointwise convergence at a chosen x.
Advanced universityPractise improper integrals, series, gradients and multiple integrals, with explicit conditions and worked examples.
Develop matrix, vector-space and projection reasoning before using least squares.
Connect initial conditions, linear differential equations and numerical approximation with error and conditioning.
Work with conditional probability, normalization, densities and expectation.
Build epsilon proofs and distinguish pointwise convergence, uniform convergence and compactness.
Study groups, vector-space quotients and proof techniques with carefully scoped definitions.
Study complex analysis, residues, Fourier series, PDE boundary conditions and convexity through focused lessons.
Learn descriptive statistics, probability, sampling and inference with worked reasoning.