PPT-Discrete Optimization

Discrete Optimization Under Uncertainty Sahil singla Institute for Advanced Study and Princeton University Oct 2 nd 2019 Example How to Sell a Diamond Sell One Diamond        potential buyers with values. Dr. Feng Gu. Way to study a system. . Cited from Simulation, Modeling & Analysis (3/e) by Law and . Kelton. , 2000, p. 4, Figure 1.1. Model taxonomy. Modeling formalisms and their simulators . Discrete time model and their simulators . Variational. Time Integrators. Ari Stern. Mathieu . Desbrun. Geometric, . Variational. Integrators for Computer Animation. L. . Kharevych. Weiwei. Y. Tong. E. . Kanso. J. E. Marsden. P. . Schr. ö. 2 EWO Problems involve Discrete/Continuous Optimization -Improved relaxations (basic steps) -NonconvexGDPs 3 Lecture 4 – Part 2. M. Pawan Kumar. pawan.kumar@ecp.fr. Finite set . Σ. called alphabet of size ≥ 2. {0,1}. {. a,b,c,d,e. ,…,. x,y,z. }. Set . Σ. * of all finite length strings (called words). Lecture 2. M. Pawan Kumar. pawan.kumar@ecp.fr. Image Segmentation. How ?. Cost/Energy function. Models . our. knowledge about natural images. Optimize . energy . function to obtain the segmentation.  . A Sampled or discrete time signal x[n] is just an ordered sequence of values corresponding to the index n that embodies the time history of the signal. A discrete signal is represented by a sequence of values x[n] ={1,2,. Large-scale Structure from Motion. David . Crandall. School of Informatics and Computing. Indiana University. Andrew Owens. CSAIL. MIT. Noah. . Snavely. . and . Dan . Huttenlocher. Department of Computer Science.  . A Sampled or discrete time signal x[n] is just an ordered sequence of values corresponding to the index n that embodies the time history of the signal. A discrete signal is represented by a sequence of values x[n] ={1,2,. Large-scale Structure from Motion. David . Crandall. School of Informatics and Computing. Indiana University. Andrew Owens. CSAIL. MIT. Noah. . Snavely. . and . Dan . Huttenlocher. Department of Computer Science. Announcements:. HW . 4. . posted, . due Tues May 8 at 4:30pm. . No late HWs as solutions will be available immediately.. Midterm details on next page. HW . 5 will . be posted . Fri May 11. , . due . Classification of algorithms. The DIRECT algorithm. Divided rectangles. Exploration and Exploitation as bi-objective optimization. Application to High Speed Civil Transport. Global optimization issues. Non-convex optimization. All loss-functions that are not convex: not very informative.. Global optimality: too strong. Weaker notions of optimality?. What is a saddle point?. Different kinds of critical/stationary points. . . Feng Luo . . Rutgers University. D. Gu (Stony Brook), J. Sun (Tsinghua Univ.), and T. Wu (Courant). Oct. 12, 2017. Geometric Analysis, . Roscoff. , France. 139252. Mechanical Engineering Department . introduction. In . mathematics. . and . computer . science. ,. . an optimization problem is the . problem. . of . finding the best solution from all feasible .