RevisedSimplex
Related Doc:
package maxima
Return the dual solution vector (y).
Return the dual solution vector (y). FIX
Find the best variable x_l to enter the basis.
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Find the best variable x_k to leave the basis given that x_l is entering.
Find the best variable x_k to leave the basis given that x_l is entering.
the variable chosen to enter the basis
Return the optimal objective function value (f(x) = c x).
Return the optimal objective function value (f(x) = c x).
the primal solution vector
Pivot by replacing x_k with x_l in the basis.
Pivot by replacing x_k with x_l in the basis. Update b_inv, b_ and c_.
the leaving variable
the entering variable
Return the primal solution vector (x).
There are M+N variables, N decision and M slack variables, of which, for each iteration, M are chosen for a Basic Feasible Solution (BFS).
There are M+N variables, N decision and M slack variables, of which, for each iteration, M are chosen for a Basic Feasible Solution (BFS). The the variables not in the basis are set to zero. Setting j to N will start with the slack variables in the basis (only works if b >= 0).
the offset to start the basis
the size of the basis
Show the current revised tableau displaying the basis, b_inv, b_, c_.
Solve a Linear Programming (LP) problem using the Revised Simplex Algorithm.
Solve a Linear Programming (LP) problem using the Revised Simplex Algorithm. Iteratively pivot until there an optimal solution is found or it is determined that the solution is unbounded.
if u <= 0., the solution is unbounded.
if u <= 0., the solution is unbounded.
the ?? vector FIX
the initial basis (set of indices where x_i is in the basis)
This class solves Linear Programming (LP) problems using the Revised Simplex Algorithm. Given a constraint matrix a, constant vector b and cost vector c, find values for the solution/decision vector x that maximize the objective function f(x), while satisfying all of the constraints, i.e.,
maximize f(x) = c x subject to a x <= b, x >= 0
The revised algorithm has benefits over the Simplex Algorithm (less memory and reduced chance of round off errors).
math.uc.edu/~halpern/Linear.progr.folder/Handouts.lp.02/Revisedsimplex.pdf