Computational Geometry Lecture Linear Programming

Transcription

1 Computational Geometry Lecture Linear Programming INSTITUTE FOR THEORETICAL INFORMATICS FACULTY OF INFORMATICS Tamara Mchedlidze Darren Strash

2 Profit optimization You are the boss of a company, that produces two products P 1 und P 2 from three raw materials R 1, R 2 und R 3. Let s assume you produce x 1 items of the product P 1 and x 2 items of product P 2. Assume that items P 1, P 2 get profit of 300e and 500e, respectively. Then the total profit is: G(x 1, x 2 ) = 300x x 2 Assume that the amout of raw material you need for P 1 and P 2 is: P 1 : 4R 1 + R 2 P 2 : 11R 1 + R 2 + R 3 And in your warehouse there are 880R 1, 150R 2 and 60R 3. So: R 1 : 4x x R 2 : x 1 + x R 3 : x 2 60 Which choice for (x 1, x 2 ) maximizes your profit? 2

3 Solution x e e e e 0 0 (300, 500) Linear constraints: R 1 : 4x x R 2 : x 1 + x R 3 : x 2 60 x 1 0 x 2 0 Linear objective function: G(x 1, x 2 ) = 300x x 2 = (300, 500) ( x 1 ) x 2 G(110, 40) = = Isocost line (orthogonal to ( ) ) x 1 Ax b x 0 max c T x c - normal vector maximum value of the objective function under the constraints. max{c T x Ax b, x 0}

4 Linear programming Definition: Given a set of linear constraints H and a linear objective function c in R d, a linear program (LP) is formulated as follows: maximize c 1 x 1 + c 2 x c d x d under constr. a 1,1 x a 1,d x d b 1 a 2,1 x a 2,d x d b 2 a n,1 x a n,d x d b n. } H H is a set of half-spaces in R d. We are searching for a point x h H h, that maximizes c T x, i.e. max{c T x Ax b, x 0}. Linear programming is a central method in operations research. 4

5 Algorithms for LPs There are many algorithms to solve LPs: Simplex-Algorithm [Dantzig, 1947] Ellipsoid-Method [Khatchiyan, 1979] Interior-Point-Method [Karmarkar, 1979] They work well in practice, especially for large values of n (number of constraints) and d (number of variables). Today: Special case d = 2 Possibilities for the solution space 5 H = infeasible c H is unbounded in the direction c feasible region H is bounded c solution is not unique c unique solution

6 First approach Idea: Compute the feasible region H and search for the angle p, that maximizes c T p. The half-planes are convex Let s try a simple Divide-and-Conquer Algorithm IntersectHalfplanes(H) if H = 1 then C H else (H 1, H 2 ) SplitInHalves(H) C 1 IntersectHalfplanes(H 1 ) C 2 IntersectHalfplanes(H 2 ) C IntersectConvexRegions(C 1, C 2 ) return C 6

7 Intersect convex regions IntersectConvexRegions(C 1, C 2 ) can be implemented using a sweep line method: consider the left and the right boundaries of C 1 and C 2 move the sweep line l from top to bottom and save the crossed edges ( 4) The nodes of C 1 C 2 define events. We process every event in O(1) time, dependent on the type of the edges incident to the event vertex. l C 1 C 2 Theorem 1: The intersection of two convex polygonal regions in the plane with n 1 + n 2 = n nodes can be computed in O(n) time. 7

8 Running time of IntersectHalfplanes(H) IntersectHalfplanes(H) if H = 1 then C H else (H 1, H 2 ) SplitInHalves(H) C 1 IntersectHalfplanes(H 1 ) C 2 IntersectHalfplanes(H 2 ) C IntersectConvexRegions(C 1, C 2 ) return C Task: What is the running time of IntersectHalfplanes(H)? Recursive formula { O(1) when n = 1 T (n) = O(n) + 2T (n/2) when n > 1 Master Theorem T (n) O(n log n) 8-3

9 Running time of IntersectHalfplanes(H) IntersectHalfplanes(H) if H = 1 then Cfeasible H region H can be found in O(n log n) time else H has O(n) nodes (H the 1, node H 2 ) p that SplitInHalves(H) maximizes c T p can therefore be found Cin 1 O(n IntersectHalfplanes(H log n) time 1 ) C 2 IntersectHalfplanes(H 2 ) C IntersectConvexRegions(C 1, C 2 ) return C Task: What is the running time of IntersectHalfplanes(H)? Recursive formula { O(1) when n = 1 T (n) = O(n) + 2T (n/2) when n > 1 Master Theorem T (n) O(n log n) 8-4

10 Bounded LPs Idea: Instead of computing the feasible region and then searching for the optimal angle, do this incrementally. Invariant: Current best solution is a unique corner of the current feasible polygon How to deal with the unbounded feasible regions? Define two half-planes for a big enough value M m 1 = { x M if c x > 0 x M otherwise When the optimal point is not unique, select lexicographically smallest one! m 2 = { y M if c y > 0 y M otherwise m 1 m2 c c 9-5

11 Bounded LPs Idea: Instead of computing the feasible region and then searching for the optimal angle, do this incrementally. Invariant: 9-6 Current best solution is a unique corner of the current feasible polygon How to deal with the unbounded feasible regions? Define two half-planes for a big enough value M m 1 = { x M if c x > 0 x M otherwise When the optimal point is not unique, select lexicographically smallest one! m 2 = { y M if c y > 0 y M otherwise Consider a LP (H, c) with H = {h 1,..., h n }, c = (c x, c y ). We denote the first i constraints by H i = {m 1, m 2, h 1,..., h i }, and the feasible polygon defineed by them by C i = m 1 m 2 h 1 h i

12 Properties each region C i has a single optimal angle v i it holds that: C 0 C 1 C n = C How the optimal angle v i 1 changes when the half plane h i is added? Lemma 1: For 1 i n and bounding line l i of h i holds that: (i) If v i 1 h i then v i = v i 1, (ii) otherwise, either C i = or v i l i. h 5 h 6 c c c v 4 v 4 = v 5 v 5 v 6 10

13 One-dimentional LP In case(ii) of Lemma 1, we search for the best point on the segment l i C i 1 : 11 we parametrize l i : y = ax + b define new objective function fc(x) i = c ( ) T x ax+b for j i 1 let σ x (l j, l i ) denote the x-coordinate of l j l i This gives us the following one-dimentional LP: maximize f i c(x) = c x x + c y (ax + b) with constr. x σ x (l j, l i ) if l i h j is limited to the right x σ x (l j, l i ) if l i h j is limited to the left Lemma 2: A one-dimentional LP can be solved in linear time. In particular, in case (ii), one can compute the new angle v i or decide whether C i = in O(i) time.

14 Incremental Algorithm 2dBoundedLP(H, c, m 1, m 2 ) worst-case running time: C 0 m 1 m 2 T (n) = n i=1 O(i) = O(n2 ) v 0 unique angle of C 0 for i 1 to n do if v i 1 h i then v i v i 1 O(1) else v i 1dBoundedLP(σ(H i 1 ), fc) i if v i = nil then O(i) return infeasible return v n Lemma 3: Algorithmus 2dBoundedLP needs Θ(n 2 ) time to solve an LP with n contraints and 2 variables. 12

15 What else can we do? Obs.: It is not the half-planes H that force the high running time, but the order in which we consider them. c h 6 h 5 h h 4 3 h 1 h 2 v 2 v 3 v 4 v 5 v 6 How to find (quickly) a good ordering? Zufällig! 13

16 Randomized incremental algorithm 2dRandomizedBoundedLP(H, c, m 1, m 2 ) C 0 m 1 m 2 v 0 unique angle of C 0 H RandomPermutation(H) for i 1 to n do if v i 1 h i then v i v i 1 else v i 1dBoundedLP(σ(H i 1 ), f i c) if v i = nil then return infeasible return v n 14

17 Random permutation RandomPermutation(A) Input: Array A[1... n] Output: Array A, rearranged into a random permutation for k n to 2 do r Random(k) Random num. between 1 and k exchange A[r] and A[k] Obs.: The running time of 2dRandomizedBoundedLP depends on the random permutation computed by the procedure RandomPermutation. In the following we compute the expected running time. Theorem 2: A two-dimentional LP with n constraints can be solved in O(n) randomized expected time. 15

18 Unbounded LPs Till now: Artificial contraints to bound C by m 1 and m 2 Next: recongnize and deal with an unbonded LP c H unbounded in the direction c Def.: A LP (H, c) is called unbounded, if there exists a ray ρ = {p + λd λ > 0} in C = H, such that the value of the objective function f c becomes arbitrarily large along ρ. It must be that: d, c > 0 d, η(h) 0 for all h H where η(h) is the normal vector of h oriented towards the feasible side of h 16

19 Characterization Lemma 4: A LP (H, c) is unbounded iff there is a vector d R 2 such that d, c > 0 d, η(h) 0 for all h H LP (H, c) with H = {h H d, η(h) = 0} is feasible. Test whether (H, c) is unbounded with a one-dimentional LP: Step 1: rotate coordinate system till c = (0, 1) normalize vector d with d, c > 0 as d = (d x, 1) For normal vector η(h) = (η x, η y ) it should hold that d, η(h) = d x η x + η y 0 Let H = {d x η x + η y 0 h H} Check whether this one-dim. LP H is feasible 17

20 Test auf Unbeschränktheit Step 2: If Step 1 returns a feasible solution d x compute H = {h H d xη x (h) + η y (h) = 0} Normals to H are orthogonal to d = (d x, 1) lines bounding half-planes of H are parallel to d intersect the bounding lines of H with x-axis 1d-LP If the two steps result in a feasible solution, the LP (H, c) is unbounded and we can construct the ray ρ. If the LP H in Step 2 is infeasible, then then so is the initial LP (H, c). If the LP H of the Step 1 is infeasible, then by Lemma 4, (H, c) is bounded. 18

21 Certificates of boundness Obs.: When the LP H = {d x η x + η y 0 h H} of the Step 1 is infeasible, we can use this information further! d right x d left x 1d-LP H is infeasible the interval [d left x, d right x ] = let h 1 and h 2 be the half planes corresponding to d left x and d right x There is no vector d that would satisfy h 1 and h 2, thus the LP ({h 1, h 2 }, c) is already bounded h 1 and h 2 are certificates of the boundness use h 1 and h 2 in 2dRandomizedBoundedLP as m 1 and m 2 19

22 Algorithms 2dRandomizedLP(H, c)? Vector d with d, c > 0 and d, η(h) 0 for all h H if d exists then H {h H d, η(h) = 0} if H feasible then return (ray ρ, unbounded) else return infeasible else (h 1, h 2 ) Certificates for the boundness of (H, c) H H \ {h 1, h 2 } return 2dRandomizedBoundedLP(H, c, h 1, h 2 ) Theorem 3: A two-dimentional LP with n constraints can be solved in O(n) randomized expected time. 20

23 Discussion Can the two-dimentional algorithms be generalized to more dimentions? Yes! The same way as the two-dimentional LP is solved incrementally with reduction to a one-dimentional LP, a d-dimentional LP can be solved by a randomized incremental algorithm with a reduction to (d 1)-dimentional LP. The expected running time is then O(c d d! n) for a constant c. The algorithm is therefore usefull only for small values on d. The simple randomized incremental algorithm for two and more dimentions given in this lecture is due to Seidel (1991). 21