Some LP problems have exactly two solutions.
False
The following LP problem has an unbounded physical region: c=x-y 4x-3y<0 3x-4y>0
False
No LP problem with an unbounded region has a solution
False
The following LP problem has an unbounded region: c=x-y 4x-3y<0 x+y<10
False
The following LP problem has an unbounded region: c=x-y 4x-3y<0 x+y>10
False
The following LP problem has an unbounded region: c=x-y 4x-3y>0 3x-4y<0
True
If a feasible region is empty, then it is bounded.
True
The solution set 2x-3y<0 is below the line 2x-3y=0
False
There is at least 3 more grams of x than y
X-y>3
There is at least 3 times the grams of x than y
X-3y>0
There is no more grams of x than y
X-y <0
The following has an empty feasible region:C=x-y4x-3y<03x-4y>0
False
In simplex method, a basic solution can assign zero to basic variables
True
In feasible solution all variables are nonnegative
True
In feasible solution all variables are positive
False
Choosing the pivot column by requiring that it be the column with the most negative entry to the left of the vert line in the last row of the tableau ensures that there's no iteration with greatest increase or decrease to objection function
True
Choosing the pivot column by requiring that it be the column with the largest positive entry to the left of the vert line in the last row of the tableau ensures that there's no iteration with greatest increase or decrease to objection function
False
In final tableau if the problem has a solution, the last column will have no negative numbers above the bottom row
True
In final tableau if the problem has a solution the last column will contain no negative numbers
False
Choosing the pivot row by requiring that the ratio associated with that row be the smallest non negative number ensures that the iteration will not take is from a feasible point to non feasible
True
The optimal value attained by the objective function for the primal problem may be different from that attained by dual
False
Dual of standard min must be a standard max
False
The dual of stand min with nonnegative obj function coefficients is a standard max problem
True
In stand max, each constraint inequality may be written so that it is less than or equal to a nonnegative number
True
In stand max, the obj function must have nonnegative coefficients
False
In standard min, the obj function must have nonnegative coefficients
False
In stand min, each constraint inequality may be written so that it is greater than or equal to any real number
True
If stand max has no solution, then the feasible region is emoty
False
If stand max has no solution then the feasible region is unbounded
True