02/17/10 CSCE 769 Optimization Homayoun Valafar Department of Computer Science and Engineering, USC.

Slides:



Advertisements
Similar presentations
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. by Lale Yurttas, Texas A&M University Chapter 14.
Advertisements

Neural and Evolutionary Computing - Lecture 4 1 Random Search Algorithms. Simulated Annealing Motivation Simple Random Search Algorithms Simulated Annealing.
Introduction to Molecular Dynamics Homayoun Valafar Department of Computer Science and Engineering, USC.
Simulated Annealing Methods Matthew Kelly April 12, 2011.
Optimization methods Review
CHAPTER 8 A NNEALING- T YPE A LGORITHMS Organization of chapter in ISSO –Introduction to simulated annealing –Simulated annealing algorithm Basic algorithm.
Random numbers and optimization techniques Jorge Andre Swieca School Campos do Jordão, January,2003 second lecture.
Classification and Prediction: Regression Via Gradient Descent Optimization Bamshad Mobasher DePaul University.
Optimization methods Morten Nielsen Department of Systems biology, DTU.
Optimization via Search CPSC 315 – Programming Studio Spring 2009 Project 2, Lecture 4 Adapted from slides of Yoonsuck Choe.
MAE 552 – Heuristic Optimization Lecture 6 February 6, 2002.
Oregon State University – CS430 Intro to AI (c) 2003 Thomas G. Dietterich and Devika Subramanian1 Search-Based Agents  Appropriate in Static Environments.
Optimization Methods One-Dimensional Unconstrained Optimization
Simulated Annealing Van Laarhoven, Aarts Version 1, October 2000.
MAE 552 – Heuristic Optimization Lecture 10 February 13, 2002.
CS 4700: Foundations of Artificial Intelligence
Optimization Methods One-Dimensional Unconstrained Optimization
Introduction to Simulated Annealing 22c:145 Simulated Annealing  Motivated by the physical annealing process  Material is heated and slowly cooled.
Optimization via Search CPSC 315 – Programming Studio Spring 2008 Project 2, Lecture 4 Adapted from slides of Yoonsuck Choe.
Molecular Modeling: Geometry Optimization C372 Introduction to Cheminformatics II Kelsey Forsythe.
By Rohit Ray ESE 251.  Most minimization (maximization) strategies work to find the nearest local minimum  Trapped at local minimums (maxima)  Standard.
Ch. 11: Optimization and Search Stephen Marsland, Machine Learning: An Algorithmic Perspective. CRC 2009 some slides from Stephen Marsland, some images.
JM - 1 Introduction to Bioinformatics: Lecture XVI Global Optimization and Monte Carlo Jarek Meller Jarek Meller Division of Biomedical.
1 Hybrid methods for solving large-scale parameter estimation problems Carlos A. Quintero 1 Miguel Argáez 1 Hector Klie 2 Leticia Velázquez 1 Mary Wheeler.
1 IE 607 Heuristic Optimization Simulated Annealing.
02/03/10 CSCE 769 Dihedral Angles Homayoun Valafar Department of Computer Science and Engineering, USC.
The Basics and Pseudo Code
MCSL Monte Carlo simulation language Diego Garcia Eita Shuto Yunling Wang Chong Zhai.
Chapter 7 Optimization. Content Introduction One dimensional unconstrained Multidimensional unconstrained Example.
Local Search Algorithms This lecture topic Chapter Next lecture topic Chapter 5 (Please read lecture topic material before and after each lecture.
B EYOND C LASSICAL S EARCH Instructor: Kris Hauser 1.
1 Unconstrained Optimization Objective: Find minimum of F(X) where X is a vector of design variables We may know lower and upper bounds for optimum No.
Simulated Annealing.
For a new configuration of the same volume V and number of molecules N, displace a randomly selected atom to a point chosen with uniform probability inside.
A Passive Approach to Sensor Network Localization Rahul Biswas and Sebastian Thrun International Conference on Intelligent Robots and Systems 2004 Presented.
CHAPTER 4, Part II Oliver Schulte Summer 2011 Local Search.
Introduction to Simulated Annealing Study Guide for ES205 Yu-Chi Ho Xiaocang Lin Aug. 22, 2000.
Introduction to Simulated Annealing Study Guide for ES205 Xiaocang Lin & Yu-Chi Ho August 22, 2000.
559 Fish 559; Lecture 5 Non-linear Minimization. 559 Introduction Non-linear minimization (or optimization) is the numerical technique that is used by.
Molecular Modelling - Lecture 2 Techniques for Conformational Sampling Uses CHARMM force field Written in C++
Simulated Annealing. Difficulty in Searching Global Optima starting point descend direction local minima global minima barrier to local search.
A local search algorithm with repair procedure for the Roadef 2010 challenge Lauri Ahlroth, André Schumacher, Henri Tokola
Chapter 10 Minimization or Maximization of Functions.
Announcement "A note taker is being recruited for this class. No extra time outside of class is required. If you take clear, well-organized notes, this.
Lecture 9 State Space Gradient Descent Gibbs Sampler with Simulated Annealing.
ZEIT4700 – S1, 2015 Mathematical Modeling and Optimization School of Engineering and Information Technology.
An Introduction to Simulated Annealing Kevin Cannons November 24, 2005.
Advanced Computer Graphics Optimization Part 2 Spring 2002 Professor Brogan.
Optimization methods Morten Nielsen Department of Systems biology, DTU IIB-INTECH, UNSAM, Argentina.
INTRO TO OPTIMIZATION MATH-415 Numerical Analysis 1.
Lecture 18, CS5671 Multidimensional space “The Last Frontier” Optimization Expectation Exhaustive search Random sampling “Probabilistic random” sampling.
Optimization via Search
Department of Computer Science
Van Laarhoven, Aarts Version 1, October 2000
High Performance Computing and Monte Carlo Methods
Local Search Algorithms
By Rohit Ray ESE 251 Simulated Annealing.
Subject Name: Operation Research Subject Code: 10CS661 Prepared By:Mrs
Maria Okuniewski Nuclear Engineering Dept.
Chapter 14.
CSE 4705 Artificial Intelligence
Introduction to Simulated Annealing
Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014
VQMC J. Planelles.
More on HW 2 (due Jan 26) Again, it must be in Python 2.7.
More on HW 2 (due Jan 26) Again, it must be in Python 2.7.
Local Search Algorithms
Local Search Algorithms
Simulated Annealing & Boltzmann Machines
Stochastic Methods.
Presentation transcript:

02/17/10 CSCE 769 Optimization Homayoun Valafar Department of Computer Science and Engineering, USC

02/17/10 CSCE 769 Optimization and Protein Folding Theoretically, the structure with the minimum total energy is the structure of interest Total energy is defined by the force-field The core of Ab Initio protein folding is optimization A robust optimization method is cruicial for successful protein folding algorithms

02/17/10 CSCE 769 Optimization Problem Given an objective (cost) function f(x) find the optimal point X* such that: Optimization is the root of most computational problems Many different approaches with their unique set of advantages and disadvantages

02/17/10 CSCE 769 Monte Carlo Easiest to implement Very effective for low dimensional problems Very ineffective for large dimensional problems Algorithm consists of randomly sampling space and accepting the point X* with the smallest value of objective function for i=1 to { X = random(range) If f(X) <= f(X*) then X*=X }

02/17/10 CSCE 769 Gradient Descent (Conjugate Gradient, Hill Climbing) Start with some initial point X 0 Calculate X k+1 from X k in the following way: Here  is the descent step size parameter  needs to be selected carefully. Too small or too large can have severe consequences. Calculate f(X k+1 ) Go to step 2.

02/17/10 CSCE 769 Gradient of A Function A multi-dimensional derivative For an n-dimensional function produces an n- dimensional vector pointing at the direction of highest increase Following the direction of the gradient will increase f(x) optimally Following in the opposite direction of the gradient will decrease f(x) optimally Example:

02/17/10 CSCE 769 Example of Gradient ascend f(x) = -x 2 - 5; x max = 0, f max = 5

02/17/10 CSCE 769 Example of an Objective (Cost) Function

02/17/10 CSCE 769 Gradient Descent

02/17/10 CSCE 769 Simulated Annealing Metropolis Algorithm

02/17/10 CSCE 769 Pseudo PASCAL code Initialize(i start, T 0, L 0 ); k := 0; i := i start ; repeat for i := 1 to L k do begin Generate(X j from X i ); if f(j) < f(i) then i := j; else if (exp(f(i) -f(j))/T k ) > random[0,1) then i := j end; k := k+1; Calculate_Control(T K ); end; Calculate_Length(L k ); until stop criterion

02/17/10 CSCE 769 Contribution of Simulated Annealing Simulated annealing helps to escape from the local minima.

02/17/10 CSCE 769 Limited Success with GD