1. A linear time algorithm for the weighted lexicographic rectilinear 1-center problem in the plane Nir Halman, Technion, Israel This work is part of my Ph.D. thesis, held in Tel Aviv University, under the supervision of Professor Arie Tamir

2. Planar 1-center problems Input: a set P={p 1,…,p n } of n points p i =(x i,y i ) a set W={w 1,…,w n } of positive weights Output: a center p=(x,y) which minimizes f(p)=Max i {w i d(p,p i )} where d is a distance function Motivation: customers, service center, 1/w i =speed d=l 2 : solvable in linear time [M83],[D86],[SW96]

3. Rectilinear 1-center (d=l 1 ) c = Min p Max i {w i (|x i -x|+|y i -y|)} Min c s.t. w i (|x i -x|+|y i -y|)  c, i=1,…,n Min c s.t. w i ((x i -x)+(y i -y))  c, i=1,…,n, w i (-(x i -x)+(y i -y))  c, i=1,…,n, w i ((x i -x)-(y i -y))  c, i=1,…,n, w i (-(x i -x)-(y i -y))  c, i=1,…,n. Transform the problem into one where d=l 

4. 1-center problem with d=l  c = Min p Max i {w i Max{|x i -x|,|y i -y|}} Min c s.t. w i |x i -x|  c, i=1,…,n, w i |y i -y|  c, i=1,…,n. Decomposition into 2 one-dimensional problems Min c x s.t. w i |x i -x|  c x i=1,…,n. Min c y s.t. w i |y i -y|  c y i=1,…,n. c=Max{c x, c y }

5. Lex 1-center problem [O97] Unique solutions to 1-dimensional problems and Euclidean planer 1-center This is not the case for planar 1-center with l 1, l  Input: a set P={p 1,…,p n } of n points p i =(x i,y i ) a set W={w 1,…,w n } of positive weights Output: a center p=(x,y) which lex-minimizes lf(p)=q(w 1 d(p,p 1 ),…,w n d(p,p n )) where q is the non-decreasing ordering of a multi-set

6. Example X Y p 1= (-4,1) p 2= (6,-1) p 4= (2,-3) p 3= (3,2) p 5= (5,0) The distances vector is lf(p)=(5,5,4,2½, 2½) No O(n log n) time algorithm exists

7. Talk outline Introduction An  (n log n) lower bound for calculating the optimal value of the optimal solution A restricted problem, the min rays problem and the relations between them Solving the min rays problem in linear time Putting it all together 

8. Lower bound Obs 1: it takes  (n log n) time to compute the distances vector of a given set of n real points Proof: reduction from sorting Given is a set R={r 1,…,r n } of n real numbers Suppose the minimal element is 0 Set P={r 1,…,r n,2r max } Center is r max Distances vector yields sorting of R

9. Talk outline Introduction An  (n log n) lower bound for calculating the optimal value of the optimal solution A restricted problem, the min rays problem and the relations between them Solving the min rays problem in linear time Putting it all together  

10. Restricted problem Input: P={p 1,…,p n }, W={w 1,…,w n } as before and a real number x @ Output: a center p @ =(x @,y) which lex-minimizes lf(p @ )=q(w 1 d(p @,p 1 ),…,w n d(p @,p n )) Set a i = w i |x @ - x i |, g i (y)=max{a i, w i |y-y i |}, then lf(p @ )=q(g 1 (y),…,g n (y )) Obs 2: Each g i (y) has ashape

11. Min rays problem Let u(y) be upper envelop of {r - i (y),r + i (y) | i=1,…,n} Goal: calculate c = min u(y)  g i (y) define r - i (y) as and r + i (y) as X Y p 1= (-4,1) p 2= (6,-1) p 4= (2,-3) p 3= (3,2) p 5= (5,0)  Y C r+3r+3 r+2r+2 r+5r+5 r+1r+1 r+4r+4 r-4r-4 r-1r-1 r-2r-2 r-3r-3

12. Restricted vs. min rays Thm 1: y $, c $ an optimal solution of the min rays problem  (x @, y $ ) is an optimal center in the corresponding restricted problem Proof: C Y C Y

13. Talk outline Introduction An  (n log n) lower bound for calculating the optimal value of the optimal solution A restricted problem, the min rays problem and the relations between them Solving the min rays problem in linear time Putting it all together   

14. Solving min rays problem Lem 1: the test oracle can be implemented in linear time Let y $, c $ be an optimal solution of the min rays problem, p @ =(x @, y $ ) be an optimal center for the corresponding restricted problem. Oracle: Input: same as for min rays problem with an additional point p =(x @, y) Output: y $ =y or y $ >y, or y $ <y

15. Solving min rays problem (cont’) Proof: partition the given 2n rays into n pairs. Group the pairs of rays into the 12 distinct sets: Thm 2: the min rays problem is solvable in linear time inc mix 4321 index type rr’ r r r r r r r

16. Solving min rays problem (cont’) Proof (cont’): r r’ r r ½ of the rays are dropped ¼ of rays are dropped 1 / 8 of rays are dropped

17. Talk outline Introduction An  (n log n) lower bound for calculating the optimal value of the optimal solution A restricted problem, the min rays problem and the relations between them Solving the min rays problem in linear time Putting it all together    

18. Algorithm: transform the input to l  solve non-lex problem solve min rays problem Generalizes to (in the same time bound): lex 1-center in R d with l  norm (quadratic dependency in d ) different weights on each of the axes all centers

19. Thank you !