Linear Programming in Small Dimensions

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1 Linear Programming in Small Dimensions Lekcija 7 sergio.cabello@fmf.uni-lj.si FMF Univerza v Ljubljani Edited from slides by Antoine Vigneron

2 Outline linear programming, motivation and definition one dimensional case a randomized algorithm in two dimension generalization to higher dimension there are deterministic algorithms also not covered. Presentation?

3 Example you can build two kinds of houses: X and Y a house of type X requires 10,000 bricks, 4 doors and 5 windows a house of type Y requires 8,000 bricks, 2 doors and 10 windows a house X can be sold $200,000 and a house Y can be sold $250,000 you have 168,000 bricks, 60 doors and 150 windows how many houses of each type should you build so as to maximize their total price?

4 Formulation x (resp. y) denotes the number of houses of type X (resp. Y ). maximize the price under the constraints f (x,y) = 200,000x + 250,000y x 0 y 0 10, 000x + 8, 000y 168, 000 4x + 2y 60 5x + 10y 150

5 Geometric interpretation 4x + 2y = 60 f (x, y) = constant Feasible region 10, 000x + 8, 000y = 168, 000 5x + 10y = 150

6 Geometric interpretation 4x + 2y = 60 optimal (x, y) 10, 000x + 8, 000y = 168, 000 5x + 10y = 150 f (x, y) = constant

7 Solution from previous frame, at the optimum x = 8 y = 11 luckily these are integers so it is the solution to our problem if we add the constraint that all variables are integers, we are doing integer programming we do not deal with it in CS4235 we consider only linear inequalities, no other constraint our example was a special case where the linear program has an integer solution, hence it is also a solution to the integer program

8 Problem statement maximize the objective function f (x 1,x 2...x d ) = c 1 x 1 + c 2 x c d x d = c x under the constraints 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 this is linear programming in dimension d

9 Geometric interpretation each constraint represents a half space in IR d the intersection of these half spaces forms the feasible region the feasible region is a convex polyhedron in IR d feasible region a constraint

10 Convex polyhedra definition: a convex polyhedron is an intersection of half spaces in IR d it is not necessarily bounded a bounded convex polyhedron is a polytope special case: a polytope in IR 2 is a convex polygon

11 Convex polyhedra in IR 3 a tetrahedron a cube a cone faces of a convex polyhedron in IR 3 vertices, edges and facets example: a cube has 8 vertices, 12 edges and 6 facets

12 Geometric interpretation let c = (c1,c 2,... c d ) we want to find a point vopt of the feasible region such that c is the outer normal at v opt if there is one vopt is the extreme point in the direction c c v opt Feasible region

13 Infeasible linear programs the feasible region can be empty in this case there is no solution to the linear program the program is said to be infeasible we would like to know when it is the case

14 Unbounded linear programs the feasible region may be unbounded in the direction of c the linear program is called unbounded in this case, we want to return a ray ρ in the feasible region with the property that f takes arbitrarily large values along ρ ρ Feasible region c

15 Degenerate cases a linear program may have an infinite number of solutions f (x, y) = opt c in this case, we report only one solution

16 Background linear programming is one of the most important problems in operations research many optimization problems in engineering and in economics are linear programs a practical algorithm: the simplex algorithm people used it without computers exponential time in the worst case there are polynomial time algorithms ellipsoid method, interior point method integer programming is NP hard we do not expect a polynomial algorithm

17 Background computational geometry techniques give good algorithms in low dimension running time is O(n) when d is constant but large dependance on d: best running time is roughly O(d 2 n) + d O( d) example of fixed-parameter algorithm this lecture: Seidel s algorithm simple, randomized expected running time O(d!n) this is O(n) when d = O(1) in practice, good for low dimension

18 One dimensional case maximize the objective function under the constraints f (x) = cx a 1 x b 1 a 2 x b 2.. a n x b n

19 Interpretation if ai > 0 then constraint i corresponds to the interval (, b ] i a i if ai < 0 then constraint i corresponds to the interval [ ) bi, a i the feasible region is an intersection of intervals so the feasible region is an interval

20 Interpretation a 1 > 0 b 1 /a 1 a 2 < 0 b 2 /a 2 L feasible region R IR

21 Algorithm assume there is (i1,i 2 ) such that a i1 < 0 < a i2 compute compute b i R = min a i >0 a i b i L = max a i <0 a i it takes O(n) time if L > R then the program in infeasible otherwise if c > 0 then the solution is x = R if c < 0 then the solution is x = L

22 Algorithm assume ai > 0 for all i compute R = min b i a i if c > 0 then the solution is x = R if c < 0 then the program is unbounded and the ray (,R] is a solution assume ai < 0 for all i compute L = max b i a i if c < 0 then the solution is x = L if c > 0 then the program is unbounded and the ray [L, ) is a solution

23 Linear programming in the plane First idea compute the feasible region intersection of halfplanes doable in O(n log n) time using duality and convex hulls the feasible region is a convex polygon find an optimal point extreme point along c can be found in O(log n) time overall, O(n log n) time this lecture: an expected O(n) time algorithm main lesson: we can find an extreme point faster than computing the whole region this difference is more dramatic in higher dimensions

24 Preliminary we only consider bounded linear programs we make sure that our linear program is bounded by enforcing two additional constraints m 1 and m 2 objective function: f (x,y) = c1 x + c 2 y let M be a large number if c1 0 then m 1 is x M if c1 0 then m 1 is x M if c2 0 then m 2 is y M if c2 0 then m 2 is y M in practice, it often comes naturally for instance, in our first example, it is easy to see that M = 30 is sufficient

25 New constraints y M m 2 c m 1 M x

26 Notation the i th constraint is it defines a half plane h i li is the line delimiting h i a i,1 x + a i,2 y b i l i h i

27 General position we assume that c is not orthogonal to any line li there is a unique solution to the linear program for any subset of constraints, also there is a unique solution to the linear subprogram can be done simulating a small rotation when there are several solutions, take the lexicographically smallest

28 Algorithm a randomized incremental algorithm we first compute a random permutation of the constraints (h 1,h 2... h n ) we denote Hi = {m 1,m 2,h 1,h 2... h i } we denote by vi a vertex of H i that maximizes the objective function in other words, vi is a solution to the linear program where we only consider the first i constraints v0 is simply the vertex of the boundary of m 1 m 2 idea: knowing vi 1, we insert h i and find v i

29 Example m 1 v 0 m 2 c f (x, y) = constant Feasible region

30 Example v 1 m 1 m 2 c f (x, y) = f (v 1 ) Feasible region h 1

31 Example v 2 = v 1 m 1 m 2 c Feasible region h 2 f (x, y) = f (v 2 ) h 1

32 Example m 1 h 3 m 2 v 3 c h 2 Feasible region f (x, y) = f (v 3 ) h 1

33 Example m 1 h 3 m 2 v 4 = v 3 c h 4 h 2 Feasible region f (x, y) = f (v 4 ) h 1

34 Algorithm randomized incremental algorithm before inserting hi, we already know v i 1 how to find vi? we distinguish two cases: vi 1 h i vi 1 h i

35 First case first case: vi 1 h i h i v i 1 c Feasible region then vi = v i 1 proof?

36 Second case second case: vi 1 h i v i 1 c Feasible region for H i 1 l i h i then vi 1 is not in the feasible region of H i then vi v i 1

37 Second case what do we know about vi? c v i Feasible region for H i 1 l i h i vi l i proof? how to find vi?

38 Second case assume ai,2 0, then the equation of l i is y = b i we replace y by bi x a i,2 in all the constraints of H i and in the objective function x a i,2 we obtain a one dimensional linear program if it is feasible, its solution gives us the x coordinate of vi we obtain the y coordinate using the equation of li if this linear program is infeasible, then the original 2D linear program is infeasible too and we are done

39 Analysis in the case vi 1 h i : we spend O(1) time in the case vi 1 h i : we spend O(i) time to solve a one dimensional linear program with i + 2 constraints so the algorithm runs in O(n 2 ) time give a worst case example where it runs in Ω(n 2 ) time what is the expected running time? we need to know how often the second case happens we define the random variable Xi Xi = 0 in first case (v i = v i 1 ) Xi = 1 in second case (v i v i 1 )

40 Analysis when Xi = 0 we spend O(1) time at i th step when Xi = 1 we spend O(i) time the expected time in the i th step is Pr[X i = 0] O(1) + Pr[X i = 1] O(i) O (1 + i Pr[X i = 1]) by linearity of expectation, the expected running time E[T(n)] of the algorithm is ( n ) E[T(n)] = O (1 + i Pr[X i = 1]) i=1

41 Analysis we denote Ci = H i in other words, Ci is the feasible region at step i vi is adjacent to two edges of C i, these edges correspond to two constraints h and h C i h v i h if vi v i 1 then h i = h or h i = h

42 Analysis what is the probability that hi = h or h i = h? we use backwards analysis we assume that Hi is fixed, no other assumption so hi is chosen uniformly at random in H i \ {m 1,m 2 } so the probability that hi = h or h i = h is 2/i so Pr[Xi = 1] 2/i and E[T(n)] = O ( n i=1 ) 1 + i 2 = O(n) i

43 Conclusion Lemma We can solve a 2-dimensional linear program with n constraints in expected O(n) time, assuming that we have a bound m 1,m 2 on the optimal solution. did we use a general position assumption like any three lines in l 1,...,l n are not coincident? can we get rid of the assumption on m1,m 2?

44 Higher dimensions each constraint is a half space can we compute their intersection and get the feasible region? in higher dimension, the feasible region has Ω(n d 2 ) vertices in the worst case computing the feasible region requires Ω(n d 2 ) time. Too much here, we will give a O(n) expected time algorithm for d = O(1)

45 Preliminary a hyperplane in IR d is a set with equation where α 1 x 1 + α 2 x α d x d = β d (α 1,α 2,...,α d ) IR d \ {0} d in general position, d hyperplanes intersect at one point each constraint hi is a half space, bounded by an hyperplane h i we assume general position in that no hyperplane is orthogonal to c

46 Algorithm we generalize the 2D algorithm we first find d constraints m1,m 2,... m d that make the linear program bounded: if ci 0 then m i is x i M if ci < 0 then m i is x i M we pick a random permutation (h1,h 2,... h n ) of H then Hi is {m 1,m 2,... m d,h 1,h 2,...h i } we maintain vi, the solution to the linear program with constraints H i and objective function f v0 is the vertex of d i=1 m i

47 Algorithm compute vertices v0,v 1,...,v n inserting hi is done in the same way as in IR 2 : if vi 1 h i then v i 1 = v i if vi 1 h i then v i h i we find vi by solving a linear program with i 1 + d constraints m 1,...,m d,h 1,...,h i 1 restricted to h i this is a (d 1)-dimensional linear program if this linear program is infeasible, then the original linear program is infeasible too, and we are done it can be done in expected O(i) time (by induction on the dimension)

48 Analysis what is the probability that vi v i 1? fix d constraints of Hi that define v i the probability that vi v i 1 is bounded by the probability that one of these d constraints was inserted last by backwards analysis, this probability is d/i so the expected running time of our algorithm is ( n ) E[T(n)] = O 1 + i d = O(dn) = O(n) i assuming that d = O(1) i=1

49 Conclusion this algorithm can be made to handle unbounded linear programs and degenerate cases a careful implementation of this algorithm runs in O(d!n) time only useful in low dimension it can be generalized to other types of problems LP-type problems (presentation?) smallest enclosing disk (presentation?) sometimes we can linearize a problem and use a linear programming algorithm enclosing annulus with minimal area

50 The big result Theorem Let d be a constant. A linear program in R d with n constraints can be solved in expected O(n) time. In particular: Corollary Let d be a constant. We can decide in expected O(n) time if the intersection of n halfspaces in R d is empty or not. If it is nonempty, we can also find a point in the intersection in the same time.

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