Solving 4 linear equations with five unknown variables?

In summary, when solving 4 linear equations with 5 unknown variables, the best strategy is to choose a specific value for one unknown and then solve for the other four. This can also be done by solving for four of the unknowns in terms of the fifth. Whether to use substitution or Gaussian elimination depends on the equations. A general rule is that the number of unknowns should be equal to the number of equations. If this is not the case, a relation can be found. To find a basis for a subspace, one does not necessarily need to show that the given set spans the entire space. A basis can still be found even if the set does not span. When using matrices, a row reduction can determine if a set of
  • #1
franz32
133
0
I hope some can help me here.

What is the best strategy in solving 4 linear equations with five unknown variables?
 
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  • #2
Well, of course, you cannot "solve" four equations in five unknowns. What you can do is select any specific value for one of the five unknowns and then solve the four equations for the other four.

Equivalently, you could solve for four of the unknowns "in terms of the fifth". That is, you treat one of the unknowns as if it were a constant and solving for the other four so that you have something of the form:
a= a function of e, b= a function of e, c= a function of e, d= a function of e, where a, b, c, d, e are the five unknowns.


Now you could choose any value you like for e and calculate the other four. Whether it is best to solve the four linear equations by substitution or Gaussian elimination depends on the particular equations.
 
  • #3
General Rule

No. of Unknown = No. of Equation

If above is not true u will end up in finding relation

As Halls has already told u about the relation
 
  • #4
Can anyone help me again

Hello guys! I did understand what you are talking about. =)
But not that really understand fully. I tell you what I don't
understand.

Let say D = {v1, v2, v3, v4, v5} where v1 = (1, 1, 0, -1); v2 = (0, 1, 2, 1); v3 = (1, 0, 1, -1); v4 = (1, 1, -6, -3) and v5 = (-1, -5, 1, 0). In order for me to find a basis for the subspace W = Span D of R^4, I must show that D spans R^4 right? If this fails, then, can I still find a basis for it?

(a,b,c,d) = k1v1 + k2v2+ k3v3 + k4v4 + k5v5. When I made an
augmented matrix out of it, I reached ... " 0 0 0 0 | b + c -3d - 4a.
What does it mean?

When do I know if a subspace does not span?
 
  • #5


Originally posted by franz32
Hello guys! I did understand what you are talking about. =)
But not that really understand fully. I tell you what I don't
understand.

Let say D = {v1, v2, v3, v4, v5} where v1 = (1, 1, 0, -1); v2 = (0, 1, 2, 1); v3 = (1, 0, 1, -1); v4 = (1, 1, -6, -3) and v5 = (-1, -5, 1, 0). In order for me to find a basis for the subspace W = Span D of R^4, I must show that D spans R^4 right? If this fails, then, can I still find a basis for it?

(a,b,c,d) = k1v1 + k2v2+ k3v3 + k4v4 + k5v5. When I made an
augmented matrix out of it, I reached ... " 0 0 0 0 | b + c -3d - 4a.
What does it mean?

When do I know if a subspace does not span?

No, in order to find a basis for subspace W= Span D, you do NOT need to show that D spans R^4.
Suppose v1= (1, 1, 0, -1), v2= (2, 2, 0, -2), v3= (3, 3, 0, -3), v4= (4, 4, 0, -4) and v5= (5, 5, 0, -5). D certainly does NOT span R^4. W= Span D is the set of all vectors of the form (a, a, 0, -a) where a is any real number. That is one-dimensional and has anyone of the vectors given as basis: {v1} will do nicely.

In the example you give, I would set this up as a matrix having each of those vectors as row and "row-reduce" I get 4 non-zero rows (the last row all zeroes) so, yes, the example you give does span all of R^4 and a perfectly good basis is {(1,0,0,0),(0,1,0,0),(0,0,1,0),(0,0,0,1)}.

If I did that with the example I gave, I would see all rows except the first become all zeroes and would know that that first row constituted the basis.
 

FAQ: Solving 4 linear equations with five unknown variables?

What is the purpose of solving 4 linear equations with five unknown variables?

The purpose of solving 4 linear equations with five unknown variables is to find a set of values for the variables that satisfy all of the equations simultaneously. This allows us to determine the relationship between the variables and make predictions based on that relationship.

What are the steps involved in solving 4 linear equations with five unknown variables?

The steps involved in solving 4 linear equations with five unknown variables are:
1. Rearrange the equations to put all variables on one side and constants on the other.
2. Use elimination or substitution to reduce the number of equations and variables.
3. Continue this process until you have only one equation and one unknown variable left.
4. Solve for the unknown variable.
5. Substitute this value back into the other equations to find the values of the remaining variables.

Can a system of 4 linear equations with five unknown variables have more than one solution?

Yes, a system of 4 linear equations with five unknown variables can have more than one solution. This means that there is more than one set of values for the variables that satisfy all of the equations. In this case, the system is said to be consistent and dependent.

What if a system of 4 linear equations with five unknown variables has no solution?

If a system of 4 linear equations with five unknown variables has no solution, then it is said to be inconsistent. This means that there is no set of values for the variables that satisfy all of the equations simultaneously. This could happen if the equations are contradictory or if there are not enough equations to solve for all of the variables.

How can solving 4 linear equations with five unknown variables be applied in real life?

Solving 4 linear equations with five unknown variables has numerous applications in real life. It can be used in engineering to model and solve systems of equations in electrical circuits, in economics to analyze supply and demand relationships, and in physics to calculate the motion of objects. It can also be used in everyday life, such as determining the cost of a grocery bill with multiple items and discounts, or finding the optimal mix of ingredients for a recipe.

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