'Local optimization' presentation slideshows

Optimisation

Optimisation. Derivative-free optimization: From Nelder-Mead to global methods. Definition. Optimizing a function is looking for the set of values of the variables that will maximize (or minimize) the function.

931 views • 80 slides

Control Flow Graphs

1.11k views • 75 slides

An Agent-Based Algorithm for Generalized Graph Colorings

An Agent-Based Algorithm for Generalized Graph Colorings. Thang N. Bui and ThanhVu H. Nguyen Computer Science Program Penn State Harrisburg. Outline. Problems Definition ABGC Algorithm Results Conclusion. Outline. Problems Definition ABGC Algorithm Results Conclusion.

729 views • 60 slides

Approximation and Visualization of Interactive Decision Maps Short course of lectures

Approximation and Visualization of Interactive Decision Maps Short course of lectures. Alexander V. Lotov Dorodnicyn Computing Center of Russian Academy of Sciences and Lomonosov Moscow State University. Lecture 7 . Non-linear Feasible Goals Method and its applications. Plan of the lecture

636 views • 53 slides

Multi-view Stereo via Volumetric Graph-cuts

Multi-view Stereo via Volumetric Graph-cuts. Philip H. S. Torr Department of Computing Oxford Brookes University. George Vogiatzis Roberto Cipolla Cambridge Univ. Engineering Dept. Multi-view Dense Stereo. Calibrated images of Lambertian scene. 3D model of scene. Volumetric.

786 views • 35 slides

CS B553: Algorithms for Optimization and Learning

CS B553: Algorithms for Optimization and Learning. Univariate optimization. f (x). x. Key Ideas. Critical points Direct methods Exhaustive search Golden section search Root finding algorithms Bisection [More next time] Local vs. global optimization Analyzing errors, convergence rates.

459 views • 31 slides

How to Stall a Motor: Information-Based Optimization for Safety Refutation of Hybrid Systems

How to Stall a Motor: Information-Based Optimization for Safety Refutation of Hybrid Systems. Todd W. Neller Knowledge Systems Laboratory Stanford University. Outline. Defining the problem: Will the critical satellite motor stall? Generalizing the problem: Hybrid Systems

412 views • 31 slides

US GLOBEC Pan-Regional Synthesis: An Outsider's View

US GLOBEC Pan-Regional Synthesis: An Outsider's View. Ken Denman Canadian Centre for Climate Modelling and Analysis Meteorological Service of Canada University of Victoria & Institute of Ocean Sciences-DFO, Sidney, BC Email: ken.denman@ec.gc.ca. U. Victoria. Outline of Talk.

370 views • 28 slides

Lecture 2: Parameter Estimation and Evaluation of Support

Lecture 2: Parameter Estimation and Evaluation of Support. Parameter Estimation.

358 views • 27 slides

Robot Motion Planning: Approaches and Research Issues

Robotics and Artificial Intelligence Laboratory Indian Institute of Information Technology, Allahabad. Robot Motion Planning: Approaches and Research Issues. Rahul Kala. Problem Solving in Mobile Robotics. Environment. Data Collection. Environment Understanding. Localization.

397 views • 26 slides

INNOVATION AND COMPLEXITY

INNOVATION AND COMPLEXITY. Carlos Eduardo Maldonado Research Professor Universidad del Rosario. INNOVATION ENTAILS COMPLEXITY. Complex systems contain and lead to surprise ( emergence) They are unpredictable ( chaotic , catastrophic )

368 views • 25 slides

POWERTRAIN DESIGN GROUP MEETING #5

POWERTRAIN DESIGN GROUP MEETING #5. Optimization of Hybrid Powertrain using DIRECT Algorithm. Sachin Kumar Porandla Advisor Dr. Wenzhong Gao. Outline. What is Optimization Local and Global Optimization Optimization Process Optimization Tools and Algorithms DIRECT algorithm

429 views • 25 slides

TSP with GAs

TSP with GAs. an example of problem analysis and optimization. TSP - by the categories. representation many choices fitness easy variation many choices selection initialization augmented algorithms. Representations. TSP is a permutation problem sequential (path) adjacent

316 views • 23 slides

Kinematic Jump Processes for Monocular 3D Human Tracking

Kinematic Jump Processes for Monocular 3D Human Tracking. Cristian Sminchisescu (University of Toronto) Bill Triggs (INRIA Rhone-Alpes). Goal: track human body motion in monocular video and estimate 3D joint motion. Why Monocular ? Movies, archival footage

315 views • 17 slides

Greedy Algorithms & Dynamic Programming

Greedy Algorithms & Dynamic Programming. Prof Amir Geva Eitan Netzer. Greedy algorithms. Solve local optimization in hope it will bring to global optimization Often trade off with the best answer possible. Huffman coding.

307 views • 12 slides

Optimizations (continue): Common subexpression elimination Code motion Strngth reduction

Optimizations (continue): Common subexpression elimination Code motion Strngth reduction Induction variable elimination Instruction selection Register allocation. Branch chaining: While c do if a then b:. L2: c If (c = false) goto L3 a If (a = false) goto L1

192 views • 12 slides

? Graded Generability in OT

? Graded Generability in OT. Problem identified by Matt Goldrick Aphasic errors predominantly k ? t but also t ? k occurs, rarely Exceptional behavior w.r.t. markedness How is this possible if *dor ? *cor in UG? Under no possible ranking can t ? k

257 views • 12 slides

Motion Capture Using Joint Skeleton Tracking and Surface Estimation

Motion Capture Using Joint Skeleton Tracking and Surface Estimation. Juergen Gall Carsten Stoll Edilson de Aguiar Christian Theobalt Bodo Rosenhahn Hans-Peter Seide CVPR 2009. Outline. Introduction Skeleton-based Pose Estimation Experiment Conclusion . Local Optimization.

286 views • 11 slides

Theory of Constraints

Theory of Constraints. Lean Manufacturing Series. Outline. Introduction to Constraints Five Steps Of Theory of Constraints Drum Buffer Rope Evaporating Cloud Method The Batch Size Problem Chain Analogy Issues with TOC Measurements & Financial Issues. Constraints.

582 views • 5 slides

Minimize: With Respect To: Subject To:

Minimize: With Respect To: Subject To:. Minimize: With Respect To: Subject To:. Minimize: With Respect To: Subject To:. Start. Minimize: With Respect To: Subject To:. Minimize: With Respect To: Subject To:.

84 views • 1 slides