# LECTURE 6: INTERIOR POINT METHOD. 1. Motivation 2. Basic concepts 3. Primal affine scaling algorithm 4. Dual affine scaling algorithm

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1 LECTURE 6: INTERIOR POINT METHOD 1. Motivation 2. Basic concepts 3. Primal affine scaling algorithm 4. Dual affine scaling algorithm

2 Motivation Simplex method works well in general, but suffers from exponential-time computational complexity. Klee-Minty example shows simplex method may have to visit every vertex to reach the optimal one. Total complexity of an iterative algorithm = # of iterations x # of operations in each iteration Simplex method - Simple operations: Only check adjacent extreme points - May take many iterations: Klee-Minty example Question: any fix?

3 Complexity of the simplex method

4 Worst case performance of the simplex method Klee-Minty Example: Victor Klee, George J. Minty, How good is the simplex algorithm? in (O. Shisha edited) Inequalities, Vol. III (1972), pp

5 Klee-Minty Example

6 Klee-Minty Example

7 Karmarkar s (interior point) approach Basic idea: approach optimal solutions from the interior of the feasible domain Take more complicated operations in each iteration to find a better moving direction Require much fewer iterations

8 General scheme of an interior point method An iterative method that moves in the interior of the feasible domain

9 Interior movement (iteration) Given a current interior feasible solution, we have > 0 An interior movement has a general format

10 Key knowledge 1. Who is in the interior? - Initial solution 2. How do we know a current solution is optimal? - Optimality condition 3. How to move to a new solution? - Which direction to move? (good feasible direction) - How far to go? (step-length)

11 Q1 - Who is in the interior? Standard for LP Who is at the vertex? Who is on the edge? Who is on the boundary? Who is in the interior?

12 What have learned before

13 Who is in the interior? Two criteria for a point x to be an interior feasible solution: 1. Ax = b (every linear constraint is satisfied) 2. x > 0 (every component is positive) Comments: 1. On a hyperplane, every point is interior relative to H. 2. For the first orthant K =, only those x > 0 are interior relative to K.

14 Example

15 How to find an initial interior solution? Like the simplex method, we have - Big M method - Two-phase method (to be discussed later!)

16 Key knowledge 1. Who is in the interior? - Initial solution 2. How do we know a current solution is optimal? - Optimality condition 3. How to move to a new solution? - Which direction to move? (good feasible direction) - How far to go? (step-length)

17 Q2 - How do we know a current solution is optimal? Basic concept of optimality: A current feasible solution is optimal if and only if no feasible direction at this point is a good direction. In other words, every feasible direction is not a good direction to move!

18 Feasible direction In an interior-point method, a feasible direction at a current solution is a direction that allows it to take a small movement while staying to be interior feasible. Observations: - There is no problem to stay interior if the step-length is small enough. - To maintain feasibility, we need

19 Good direction In an interior-point method, a good direction at a current solution is a direction that leads it to a new solution with a lower objective value. Observations:

20 Optimality check Principle: no feasible direction at this point is a good direction.

21 Key knowledge 1. Who is in the interior? - Initial solution 2. How do we know a current solution is optimal? - Optimality condition 3. How to move to a new solution? - Which direction to move? (good feasible direction) - How far to go? (step-length)

22 Q3 How to move to a new solution? 1. Which direction to move? - a good, feasible direction Good requires Feasible requires Question: any suggestion?

23 A good feasible direction Reduce the objective value Maintain feasibility

24 Projection mapping A projection mapping projects the negative gradient vector c into the null space of matrix A

25 Q3 How to move to a new solution? 2. How far to go? - To satisfy every linear constraint Since the step-length can be real number. - To stay to be an interior solution, we need

26 How to choose step-length? One easy approach - in order to keep > 0 we may use the minimum ratio test to determine the step-length. Observation: - when is close to the boundary, the step-length may be very small. Question: then what?

27 Observations If a current solution is near the center of the feasible domain (polyhedral set), in average we can make a decently long move. If a current solution is not near the center, we need to re-scale its coordinates to transform it to become near the center". Question: but how?

28 Where is the center? We need to know where is the center of the non-negative/first orthant. -Concept of equal distance to the boundary Question: If not, what to do?

29 Concept of scaling Scale Define a diagonal scaling matrix

30 Transformation affine scaling Affine scaling transformation The transformation is 1. one-to-one 2. onto 3. Invertible 4. boundary to boundary 5. interior to interior

31 Transformed LP

32 Step-length in the transformed space Minimum ratio test in the y-space

33 Property 1 Iteration in the x-space

34 Property 2 Feasible direction in x-space

35 Property 3 Good direction in x-space

36 Property 4 Optimality check (Lemma 7.2)

37 Property 5 Well-defined iteration sequence (Lemma 7.3) From properties 3 and 4, if the standard form LP is bounded below and is not a constant, then the sequence is well-defined and strictly decreasing.

38 Property 6 Dual estimate, reduced cost and stopping rule We may define

39 Property 7 Moving direction and reduced cost

40 Primal affine scaling algorithm

41 Example

42 Example

43 How to find an initial interior feasible solution? Big-M method Idea: add an artificial variable with a big penalty (big-m) Objective

44 Properties of (big-m) problem (1) It is a standard form LP with n+1 variables and m constraints. (2) e is an interior feasible solution of (big-m). (3)

45 Two-phase method

46 Properties of (Phase-I) problem

47 Facts of the primal affine scaling algorithm

48 More facts

49 Improving performance potential push Idea: (Potential push method) - Stay away from the boundary by adding a potential push. Consider Use

50 Improving performance logarithmic barrier Idea: (Logarithmic barrier function method) Consider Properties: (2)

51 Dual affine scaling algorithm Affine scaling method applied to the dual LP Idea:

52 Key knowledge Dual scaling (centering) Dual feasible direction Dual good direction increase the dual objective value Dual step-length Primal estimate for stopping rule

53 Observation 1 Dual scaling (centering)

54 Observation 2 Dual feasibility (feasible direction)

55 Observation 3 Increase dual objective function (good direction)

56 Observation 4 Dual step-length

57 Observation 5 Primal estimate We define

58 Dual affine scaling algorithm

59 Find an initial interior feasible solution

60 Properties of (big-m) problem

61 Performance of dual affine scaling No polynomial-time proof! Computational bottleneck Less sensitive to primal degeneracy and numerical errors, but sensitive to dual degeneracy. Improves dual objective value very fast, but attains primal feasibility slowly.

62 Improving performance 1. Logarithmic barrier function method Polynomial-time proof

63 Improving performance Power series method - Basic idea: following a higher order trajectory

64 Power series expansion

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