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##### ISM 206 Lecture 3

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**ISM 206Lecture 3**The Simplex Method**Announcements**• Homework due 6pm Thursday • Thursday 6pm lecture**Outline**• LP so far • Correction: Standard form • Why we can look only at basic feasible solutions • Optimality conditions • The simplex method • The step from one bfs to the next • Tableu method • Phase I: Finding an initial BFS**LP so far**• Formulated LP’s in various contexts • Transform any LP into a standard form LP • Intuition of simplex method: Find the best corner point feasible solution • Math required: • Corner point Feasible or basic feasible solutions correspond to a set of n active constraints • Any set of active constraints corresponds to a basis from the matrix A • The basis is a set of linearly independent columns**Standard Form**Concise version: A is an m by n matrix: n variables, m constraints**Solutions, Extreme points and bases**• Key fact: • If a LP has an optimal solution, then it has an optimal extreme point solution (proved today) • Basic Feasible solution (Corner Point Feasible): • The vector x is an extreme point of the solution space iff it is a bfs of Ax=b, x>=0 • If A is of full rank then there is at least one basis B of A • B is set of linearly independent columns of A • B gives us a basic solution • If this is feasible then it is called a basic feasible solution (bfs) or corner point feasible (cpf)**Optimal basis theorem**Theorem If a LP in standard form has a finite optimal solution then it has an optimal basic feasible solution Proof Requires the representation theorem…**Simplex Method**• Checks the corner points • Gets better solution at each iteration 1. Find a starting solution 2. Test for optimality • If optimal then stop 3. Perform one iteration to new CPF (BFS) solution. Back to 2.**Simplex Method: basis change**• One basic variable is replaced by another • The optimality test identifies a non-basic variable to enter the basis • The entering variable is increased until one of the other basic variables becomes zero • This is found using the minimum ratio test • That variable departs the basis**Standard Form to Augmented Form**A is an m by n matrix: n variables, m constraints**The simplex method**• Example • Table version**A basic feasible solution**• B=basis of A. • Write LP in terms of basis X is a basic solution of the LP X is a basic feasible solution if it is feasible! (example)**Optimality of a basis**We want to test of a basic feasible solution is optimal Use the basic notation from before**Finding an initial bfs**• The ‘phase 1’ approach • The ‘big M’ method**Proof that the Simplex method works**• If there exists an optimal point, there exists an optimal basic feasible solution • There are a finite number of bfs • Each iteration, the simplex method moves from one bfs to another, and always improves the objective function value • Therefore the simplex method must converge to the optimal solution (in at most S steps, where S is the number of basic feasible solutions)