14-240/Tutorial-October28: Difference between revisions

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==Boris==
==Boris==


====Avoid the Einstellung Effect====
====Be Efficient====


By this point in the course, we become good at solving systems of linear equations. However, we should not use this same old problem-solving strategy over and over if a more efficient one exists. Consider the following problems:
By this point in the course, we become good at solving systems of linear equations. However, we should not use this same old problem-solving strategy over and over if a more efficient one exists. Consider the following problems:

Latest revision as of 11:37, 29 November 2014

Boris

Be Efficient

By this point in the course, we become good at solving systems of linear equations. However, we should not use this same old problem-solving strategy over and over if a more efficient one exists. Consider the following problems:


Q1: Determine if is linearly independent in .

We can solve this linear equation to find the answer:


where .


Yet there is a less time-consuming approach that relies on two observations:

(1) The dimension of is so the size of a basis is also .
(2) No linearly independent set can have more vectors than a generating set (by the Replacement Theorem).

Since a basis is a generating set and the size of is , then the Replacement Theorem tells us that cannot be linearly independent. Hence, the problem can be solved without solving any linear equations.


Q2: Determine if the polynomials generate .

Once again, we can solve a linear equation but we do not have to. Observe:

(1) The dimension is so the size of a basis is also .
(2) No generating set can have fewer vectors than a basis (by a Corollary to the Replacement Theorem).

Since there are only polynomials, then the Corollary tells us that it cannot generate . Once again, we used a more efficient strategy.


Extending a Linearly Independent Set to a Basis

Boris's tip (for concrete sets and vector spaces only):


If a problem requires us to extend a linearly independent set to a basis, then the easiest approach is to add vectors from the standard ordered basis. Here is an example:


Let be a linearly independent subset of . To extend to a basis, add vectors from . The only question is which vector(s) should we add?


We see that both vectors in have a as the third component so a safe choice is to add . Since has a dimension of , then is a basis of .

Nikita