1. Multiplicative weights method: A meta algorithm with applications to linear and semi-definite programming Sanjeev Arora Princeton University Based upon: Fast algorithms for Approximate SDP [FOCS ‘05]  log(n) approximation to SPARSEST CUT in Õ(n 2 ) time [FOCS ‘04] The multiplicative weights update method and it’s applications [’05] See also recent papers by Hazan and Kale.

2. Multiplicative update rule (long history) Applications: approximate solutions to LPs and SDPs, flow problems, online learning (boosting), derandomization & chernoff bounds, online convex optimization, computational geometry, metricembeddongs, portfolio management… (see our survey) w1w2...wnw1w2...wn n agentsweights Update weights according to performance: w i t+1 Ã w i t (1 +  ¢ performance of i)

3. Simplest setting – predicting the market N “experts” on TV Can we perform as good as the best expert ? 1$ for correct prediction 0$ for incorrect

4. Weighted majority algorithm [LW ‘94] Multiplicative update (initially all w i =1): If expert predicted correctly: w i t+1 Ã w i t If incorrectly, w i t+1 Ã w i t (1 -  ) Claim: #mistakes by algorithm ¼ 2(1+  )(#mistakes by best expert) Potential:  t = Sum of weights=  i w i t (initially n) If algorithm predicts incorrectly )  t+1 ·  t -   t /2  T · (1-  ) m(A) n m(A) =# mistakes by algorithm  T ¸ (1-  ) m i ) m(A) · 2(1+  )m i + O(log n/  ) “Predict according to the weighted majority.”

5. Generalized Weighted majority [A.,Hazan, Kale ‘05] n agents Set of events (possibly infinite) event j expert i payoff M(i,j)

6. Generalized Weighted majority [AHK ‘05] n agents Set of events (possibly infinite) Claim: After T iterations, Algorithm payoff ¸ (1-  ) best expert – O(log n /  ) Algorithm: plays distribution on experts (p 1,…,p n ) Payoff for event j:  i p i M(i,j) Update rule: p i t+1 Ã p i t (1 +  ¢ M(i,j)) p1p2...pnp1p2...pn

7. Game playing, Online optimization Lagrangean relaxation Gradient descent Chernoff bounds Games with Matrix Payoffs Fast soln to LPs, SDPs Boosting

8. Common features of MW algorithms “competition” amongst n experts Appearance of terms like exp( -  t (performance at time t) ) Time to get  -approximate solutions is proportional to 1/  2.

9. Boosting and AdaBoost (Schapire’91, Freund-Schapire’97) “Weak learning ) Strong learning” Input: 0/1 vectors with a “Yes/No” label (white, tall, vegetarian, smoker,…,) “Has disease” (nonwhite, short, vegetarian, nonsmoker,..) “No disease” Desired: Short OR-of-AND (i.e., DNF) formula that describes  fraction of data (if one exists) (white Æ vegetarian) Ç (nonwhite Æ nonsmoker)  = 1-  “Strong Learning”  = ½ +  “Weak Learning” Main idea in boosting: data points are “experts”, weak learners are “events.”

10. Approximate solutions to convex programming e.g., Plotkin Shmoys Tardos ’91, Young’97, Garg Koenemann’99, Fleischer’99 MW Meta-Algorithm gives unified view Note: Distribution $ Convex Combination { p i }  i p i =1 If P is a convex set and each x i 2 P then  i p i x i 2 P

11. Solving LPs (feasibility) a 1 ¢ x ¸ b 1 a 2 ¢ x ¸ b 2  a m ¢ x ¸ b m x 2 P  k w k (a k ¢ x – b k ) ¸ 0 x 2 P P = convex domain -   -  w1w2wmw1w2wm Event Oracle

12. Solving LPs (feasibility) a 1 ¢ x ¸ b 1 a 2 ¢ x ¸ b 2  a m ¢ x ¸ b m x 2 P w1w2wmw1w2wm [1 –  (a 1 ¢ x 0 – b 1 )/  ] £ [1 –  (a 2 ¢ x 0 – b 2 )/  ] £  [1 –  (a m ¢ x 0 – b m )/  ] £ x0x0 |a k ¢ x 0 – b k | ·   = width  k w k (a k ¢ x – b k ) ¸ 0 x 2 P Oracle Final solution = Average x vector

13. Performance guarantees In O(  2 log(n)/  2 ) iterations, average x is  feasible. Packing-Covering LPs: [Plotkin, Shmoys, Tardos ’91] 9 ? x 2 P: j = 1, 2, … m: a j ¢ x ¸ 1 Want to find x 2 P s.t. a j ¢ x ¸ 1 -  Assume: 8 x 2 P: 0 · a j ¢ x ·  MW algorithm gets  feasible x in O(  log(n)/  2 ) iterations Covering problem

14. Connection to Chernoff bounds and derandomization Deterministic approximation algorithms for 0/1 packing/covering problem a la Raghavan-Thompson Solve LP relaxation of integer program. Obtain soln (y i ) Randomized Rounding O(log n) times) x i à 1 w/ prob. y i x i à 0 w/ prob.1 - y i Derandomize using pessimistic estimators exp(  i t ¢ f(y i )) Young [95] “Randomized rounding without solving the LP.” MW update rule mimics pessimistic estimator.

15. Semidefinite programming ( Klein-Lu’97 ) a 1 ¢ x ¸ b 1 a 2 ¢ x ¸ b 2  a m ¢ x ¸ b m x 2 P P = a j and x : symmetric matrices in R n £ n {x: x is psd; tr(x) =1} Oracle: max  j w j (a j ¢ x) over P (eigenvalue computation!) Application 2:

16. The promise of SDPs… 0.878-approximation to MAX-CUT (GW’94), 7/8-approximation to MAX-3SAT (KZ’00) p log n –approximation to SPARSEST CUT, BALANCED SEPARATOR (ARV’04). p log n-approximation to MIN-2CNF DELETION, MIN-UNCUT (ACMM’05), MIN-VERTEX SEPARATOR (FHL’06),… etc. etc…. The pitfall… High running times ---as high as n 4.5 in recent works

17. Solving SDP relaxations more efficiently using MW [AHK’05] Problem Using Interior Point Our result M AX QP (e.g. M AX - C UT ) Õ(n 3.5 ) Õ(n 1.5 N/  2.5 ) or Õ(n 3 /  *  3.5 ) H APLO F REQ Õ(n 4 ) Õ(n 2.5 /  2.5 ) SCPÕ(n 4 ) Õ(n 1.5 N/  4.5 ) E MBEDDING Õ(n 4 ) Õ(n 3 /d 5  3.5 ) S PARSEST C UT Õ(n 4.5 ) Õ(n 3 /  2 ) M IN U NCUT etcÕ(n 4.5 )Õ(n 3.5 /  2 )

18. Key difference between efficient and not-so-efficient implementations of the MW idea: Width management. (e.g., the difference between PST’91 and GK’99)

19. Recall: issue of width Õ(  2 /  2 ) iterations to obtain  feasible x  = max k |a k ¢ x – b k |  is too large!! MW a 1 ¢ x ¸ b 1 a 2 ¢ x ¸ b 2  a m ¢ x ¸ b m Oracle  k w k (a k ¢ x – b k ) ¸ 0 x 2 P

20. Issue 1:Dealing with width A few high width constraints Run a hybrid of MW and ellipsoid/Vaidya poly(m, log(  /  )) iterations to obtain  feasible x MW a 1 ¢ x ¸ b 1 a 2 ¢ x ¸ b 2  a m ¢ x ¸ b m Oracle  k w k (a k ¢ x – b k ) ¸ 0 x 2 P

21. Hybrid of MW and Vaidya Õ(  L 2 /  2 ) iterations to obtain  feasible x L ¿ L ¿  MW a 1 ¢ x ¸ b 1 a 2 ¢ x ¸ b 2  a m ¢ x ¸ b m Oracle  k w k (a k ¢ x – b k ) ¸ 0 x 2 P Dual ellipsoid/Vaidya Dealing with width (contd)

22. Issue 2: Efficient implementation of Oracle: fast eigenvalues via matrix sparsification Lanczos algorithm effectively uses sparsity of C Similar to Achlioptas, McSherry [’01], but better in some situations (also easier analysis) C O(  n  ij |C ij |/  ) O(  n  ij |C ij |/  ) non-zero entries k C – C’ k ·  Random sampling C’

23. O(n 2 )-time algorithm to compute O( p log n)-approximation to SPARSEST CUT [A., Hazan, Kale ’04] (combinatorial algorithm; improves upon O(n 4.5 ) algorithm of A, Rao, Vazirani)

24. Sparsest Cut S O(log n) approximation [Leighton Rao ’88] O( p log n) approximation [A., Rao, Vazirani’04] O( p log n) approximation in O(n 2 ) time. (Actually, finds expander flows) [A., Hazan, Kale’05] The sparsest cut: S G) = 2/5

25. MW algorithm to find expander flows Events – { (s,w,z) | weights on vertices, edges, cuts} Experts – pairs of vertices (i,j) Payoff: (for weights d i,j on experts) Fact: If events are chosen optimally, the distribution on experts d i,j converges to a demand graph which is an “expander flow” [by results of Arora-Rao-Vazirani ’04 suffices to produce approx. sparsest cut] shortest path according to weights w e Cuts separating i and j

26. New: Online games with matrix payoffs (joint w/ Satyen Kale’06) Payoff is a matrix, and so is the “distribution” on experts! Uses matrix analogues of usual inequalities 1+ x · e x I + A · e A Can be used to give a new “primal-dual” view of SDP relaxations ( ) easier, faster approximation algorithms)

27. New: Faster algorithms for online learning and portfolio management (Agarwal-Hazan’06, Agarwal-Hazan-Kalai-Kale’06 ) Framework for online optimization inspired by Newton’s method (2 nd order optimization). (Note: MW ¼ gradient descent) Fast algorithms for Portfolio management and other online optimization problems

28. Open problems Better approaches to width management? Faster run times? Lower bounds? THANK YOU

29. Connection to Chernoff bounds and derandomization Deterministic approximation algorithms a la Raghavan-Thompson Packing/covering IP with variables x i = 0/1 9 ? x 2 P: 8 j 2 [m], f j (x) ¸ 0 Solve LP relaxation using variables y i 2 [0, 1] Randomized rounding: x i = Chernoff: O(log m) sampling iterations suffice 1 w.p. y i 0 w.p. 1 – y i

30. Derandomization [Young, ’95] Can derandomize the rounding using exp(t  j f j (x)) as a pessimistic estimator of failure probability By minimizing the estimator in every iteration, we mimic the random expt, so O(log m) iterations suffice The structure of the estimator obviates the need to solve the LP: Randomized rounding without solving the Linear Program Punchline: resulting algorithm is the MW algorithm!

31. Weighted majority [LW ‘94] If lost at t,  t+1 · (1-½  )  t At time T:  T · (1-½  ) #mistakes ¢  0 Overall: #mistakes · log(n)/  + (1+  ) m i #mistakes of expert i

32. Semidefinite programming Vectors a j and x: symmetric matrices in R n £ n x º 0 Assume: Tr(x) · 1 Set P = {x: x º 0, Tr(x) · 1} Oracle: max  j w j (a j ¢ x) over P Optimum: x = vv T where v is the largest eigenvector of  j w j a j

33. Efficiently implementing the oracle Optimum: x = vv T v is the largest eigenvector of some matrix C Suffices to find a vector v such that v T Cv ¸ 0 Lanczos algorithm with a random starting vector is ideal for this Advantage: uses only matrix-vector products Exploits sparsity (also: sparsification procedure) Use analysis of Kuczynski and Wozniakowski [’92]