PPT-An O(1) Approximation Algorithm for Generalized Min-Sum Set

Ravishankar Krishnaswamy Carnegie Mellon University joint work with Nikhil Bansal IBM and Anupam Gupta CMU elgooG A Hypothetical Search Engine Given a search query Q Identify relevant webpages and order them. 99 4069 3629 3489 3339 3199 3159 3289 3429 3569 3969 4379 50 M Free 14309 13219 12119 11749 11389 11019 10999 11339 11689 12039 13089 14129 100 M Free 34849 32339 25839 25009 24169 23339 23039 23749 24469 25189 31329 33479 200 M Free 73729 65159 6058 2 02 02 02 Flash point TOC F T 79 100 150 150 150 Distillation test T 78 Distillate percentage by volume of tota l distillate to 680F to 437F 25 10 to 500F 40 70 15 55 35 15 to 600F 75 93 60 87 45 80 15 75 Residue from distillation volume A Worst-Case Analysis. L. . Bertazzi. , B. Golden, and X. Wang. Route 2014. Denmark. June 2014 . 1. Introduction. In the min-sum VRP, the objective is to minimize the total cost incurred over all the routes. Prasad . Raghavendra. . Ning. Chen C. . . Thach. . Nguyen . . . Atri. . Rudra. . . Gyanit. Singh. University of Washington. Roee . Engelberg. Technion. University. Matt Weinberg. MIT .  Princeton  MSR. References: . . http. ://arxiv.org/abs/. 1305.4002. http. ://arxiv.org/abs/. 1405.5940. http. ://arxiv.org/abs/. 1305.4000. Recap. Costis. ’ Talk: . Optimal multi-dimensional mechanism: additive bidders, no constraints. Princeton University. Game Theory Meets. Compressed Sensing. Based on joint work with:. Volkan. Cevher. Robert. Calderbank. Rob. Schapire. Compressed Sensing. Main tasks:. Design a . sensing . matrix. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. 1. Tsvi. . Kopelowitz. Knapsack. Given: a set S of n objects with weights and values, and a weight bound:. w. 1. , w. 2. , …, w. n. , B (weights, weight bound).. v. 1. , v. 2. , …, v. n. (values - profit).. Alexander . Veniaminovich. IM. , . room. . 3. 44. Friday. 1. 7. :00. or. Saturday 14:30. Approximation. . algorithms. . 2. We will study. . NP. -. hard optimization problem. 3. What you should know. Sometimes we can handle NP problems with polynomial time algorithms which are guaranteed to return a solution within some specific bound of the optimal solution. within a constant . c. . of the optimal. Algorithms. and Networks 2015/2016. Hans L. . Bodlaender. Johan M. M. van Rooij. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. A. A. What to do if a problem is. Cost . Flow, Kevin D. Wayne. Eyal Dushkin – 03.06.13. Reminder – Generalized Flows. We are given a graph . We associate a positive . with every . arc. Assume that if 1 unit of flow was sent from node . The Lagrangian. Holonomic constraints. Generalized coordinates. Nonholonomic constraints. Euler-Lagrange equations. Hamilton’s equations. Generalized forces. we haven’t done this,. so let’s start with it. David P. Williamson. Joint work with Matthias Poloczek (Cornell), Georg Schnitger (Frankfurt), and Anke van Zuylen (William & Mary). Greedy algorithms. “Greed. , for lack of a better word, is good. Greed is right. Greed works.