Simplex method Modules

Compute simplex method functions

simplex_networks.create_matrix(variables, …)	Creates a matrix with enough rows for each constraint plus the objective function and enough columns for all the variables.
simplex_networks.pivots_col(matrix)	Checks to see if pivots are required due to negative values in right column, excluding the bottom value.
simplex_networks.pivots_row(matrix)	Checks to see if pivots are required due to negative values in bottom row, excluding the final value.
simplex_networks.find_negative_col(matrix)	Finds location of negative values in right column.
simplex_networks.find_negative_row(matrix)	Finds location of negative values in bottom row.
simplex_networks.find_pivot_col(matrix)	Finds pivot element corresponding to a negative value in right column.
simplex_networks.find_pivot_row(matrix)	Finds pivot element corresponding to a negative value in bottom row.
simplex_networks.pivot(row, col, matrix)	Pivot about a value to remove negative in final column or row.
problem_definition.add_cons(matrix)	Checks if 1 extra constraint can be added to the matrix, this means that there are at least two rows of all 0 elements.
problem_definition.constrain(matrix, eq[, …])	Adds constraints to the problem.
problem_definition.add_obj(matrix)	Verifies if the objective function can be added.
problem_definition.obj(matrix, eq[, memory_prof])	Adds the objective function to the problem matrix.
problem_definition.maxz(matrix[, aux])	Creates maximization function.
problem_definition.convert_min(matrix)	This function multiplies by -1 the objective function for maximization problems.
problem_definition.minz(matrix)	Creates minimization function.
problem_definition.convert(eq)	Converts equation into a list containing the coefficients of the equation.