Linear programming standard form and simplex tableaus are crucial tools for solving optimization problems. They provide a structured approach to organizing and analyzing complex systems, allowing for efficient problem-solving and decision-making.

Understanding how to convert problems to standard form and construct initial tableaus is essential. These skills enable you to tackle a wide range of real-world optimization challenges, from resource allocation to production planning.

Linear Programming Standard Form and Simplex Tableau

Conversion to standard form

• Identify objective function, constraints, and decision variables in linear programming problems
• Convert maximization to minimization (or vice versa) by multiplying objective function by -1
• Transform inequality constraints to equality constraints by adding slack variables for $\leq$ constraints (x + s = b) and subtracting surplus variables for $\geq$ constraints (x - s = b)
• Ensure non-negativity of variables by splitting free variables into positive and negative components (x = x+ - x-)
• Recognize standard form: all constraints are equations, all variables non-negative, objective function in desired form (max or min)

Construction of initial tableaus

• Organize standard form elements into tableau: objective function coefficients in top row, constraint coefficients in subsequent rows, right-hand side values in last column
• Add identity matrix for slack variables (1 in corresponding row, 0 elsewhere)
• Include column for basic variables (initially slack variables)
• Set up initial basic feasible solution with slack variables as initial basic variables and initial objective function value (typically 0)

Components of simplex tableaus

• Objective function row (z-row) contains coefficients of decision and slack/surplus variables
• Constraint rows list coefficients of decision and slack/surplus variables
• Right-hand side (RHS) column shows values of constraints
• Basic variable column lists current basic variables
• Objective function value located in bottom-right cell of tableau

Role of slack variables

• Convert inequality constraints to equality constraints and represent unused resources
• Non-negative variables added to $\leq$ constraints with coefficient of +1
• Increase number of variables without changing feasible region
• Provide initial basic feasible solution and allow pivoting in simplex algorithm
• Measure difference between left and right sides of inequality constraints
• Indicate unused resources at given solution (s = 5 means 5 units of resource not used)