'Convex set' diaporamas de présentation

The Simplex Algorithm

The Simplex Algorithm. ???. ??????? ?????????. Content. Basic Feasible Solutions The Geometry of Linear Programs Moving From Bfs to Bfs Organization of a Tableau Choosing a Profitable Column Pivot Selection Anticycling Algorithm The Two-Phase Simplex Algorithm. Linear Programming.

1.89k views • 146 slides

My great amigo

I sincerely thank. My great amigo. Zoli. for being Zoli. I sincerely thank. this amigo. and. this amigo. for the kind invite. I wish to, humbly and most sincerely, thank the. The Society that publishes 2 of the very best. and holds meetings in exotic places.

1.27k views • 109 slides

Register Allocation

Register Allocation. ?????. 5. Outline. What is register allocation Webs ? Interference Graphs( ???? Graph coloring ???????? Spilling ???? Splitting ???? More optimizations. Storing values between def and use. Program computes with values value definitions (where computed)

1.16k views • 97 slides

POCS:

POCS:. A Specialized Application Tool for Signal Synthesis & Analysis Robert J. Marks II, CIA Lab Department of Electrical Engineering University of Washington Seattle, WA 98036 marks@ee.washington.edu.

1.04k views • 81 slides

Convex Programming

Convex Programming. Brookes Vision Reading Group. Huh?. What is convex ??? What is programming ??? What is convex programming ???. Huh?. What is convex ??? What is programming ??? What is convex programming ???. Convex Function. f(t x + (1-t) y) <= t f(x) + (1-t) f(y). Convex Function.

863 views • 71 slides

Convex Programming

Convex Programming. Brookes Vision Reading Group. Huh?. What is convex ??? What is programming ??? What is convex programming ???. Huh?. What is convex ??? What is programming ??? What is convex programming ???. Convex Function. f(t x + (1-t) y) <= t f(x) + (1-t) f(y). Convex Function.

828 views • 71 slides

Support Vector Machines

Support Vector Machines. Text Book Slides. Find a linear hyperplane (decision boundary) that will separate the data. Support Vector Machines. One Possible Solution. Support Vector Machines. Another possible solution. Support Vector Machines. Other possible solutions.

1.21k views • 69 slides

Clustering on the Simplex

424 views • 34 slides

???????????? A ???? (2011/01/14)

471 views • 29 slides

???????????? A ???? (2012/01/13)

???????????? A ???? (2012/01/13). ?????????? ??????????????? ??????????????????? ?????????????????? ??????. ???????????? Wagner-Whitin ? WW) ???. ???? T =6 ?????? d t t ?????????????????????????????? a t ? 100 ?????? t t ??????????????????????????? v t ?????? t

656 views • 26 slides

Backgrounds-Convexity

Backgrounds-Convexity. Def: line segment joining two points is the collection of points. Def: is called convex set iff whenever. Nonconvex set. Convex sets.

251 views • 25 slides

Chapter 2

Chapter 2. Consumers and Their Preferences. Figure 2.1 The Consumption Possibility Set. Figure 2.2 A Convex Set. Figure 2.3 The Economically Feasible Consumption Set: Time Constraints. Figure 2.4 The Economically Feasibly Consumption Set: Income Constraints.

335 views • 22 slides

Theory of Algorithms: Brute Force

Theory of Algorithms: Brute Force. Outline. Examples Brute-Force String Matching Closest-Pair Convex-Hull Exhaustive Search brute-force strengths and weaknesses. Brute Force. A straightforward approach usually directly based on problem statement and definitions Motto: Just do it!

527 views • 22 slides

Bayesianism , Convexity, and the quest towards Optimal Algorithms

Bayesianism , Convexity, and the quest towards Optimal Algorithms. Boaz Barak. Microsoft Research. Harvard University. Partially based on work in progress with Sam Hopkins , Jon Kelner, Pravesh Kothari , Ankur Moitra and Aaron Potechin. Talk Plan. Dubious historical analogy.

222 views • 22 slides

Computational Geometry The art of finding algorithms for solving geometrical problems

Computational Geometry The art of finding algorithms for solving geometrical problems. Literature: M. De Berg et al: Computational Geometry, Springer, 2000 [1] H. Edelsbruner: Algorithms in Combinatorial Geometry, Springer, 1987[2]

329 views • 20 slides

MATHEMATICS 3 Operational Analysis

285 views • 15 slides

Computational Geometry The art of finding algorithms for solving geometrical problems

Computational Geometry The art of finding algorithms for solving geometrical problems. Literature: M. De Berg et al: Computational Geometry, Springer, 2000. H. Edelsbruner: Algorithms in Combinatorial Geometry, Springer, 1987. 1. Convex Hull. 1.1 Euclidean 2-dimensional space E 2

327 views • 15 slides

Chapter 3: Brute Force

The Design and Analysis of Algorithms. Chapter 3: Brute Force. Chapter 3. Brute Force Algorithms. Basic Idea Brute Force Search and Sort Brute Force String Matching Closest Pair and Convex Hull Problems Exhaustive Search Conclusion. Basic Idea.

352 views • 12 slides

Computational Geometry The art of finding algorithms for solving geometrical problems

Computational Geometry The art of finding algorithms for solving geometrical problems. Literature: M. De Berg et al: Computational Geometry, Springer, 2000. H. Edelsbruner: Algorithms in Combinatorial Geometry, Springer, 1987. 1. Convex Hull. 1.1 Euclidean 2-dimensional space E 2

313 views • 12 slides

271 views • 11 slides