PPT-Survey of unconstrained optimization gradient based algorithms

Unconstrained minimization Steepest descent vs conjugate gradients Newton and quasiNewton methods Matlab fminunc Unconstrained local minimization The necessity for one dimensional searches. Regrets and . Kidneys. Intro to Online Stochastic Optimization. Data revealed over time. Distribution . of future events is known. Under time constraints. Limits amount of . sampling/simulation. Solve these problems with two black boxes:. Combinatorial and Graph Algorithms. Welcome!. CS5234 Overview. Combinatorial & Graph Algorithms. http://. www.comp.nus.edu.sg/~cs5234/. Instructor: . Seth Gilbert. Office: . COM2-204. Office hours: . Select algorithms based on their popularity.. Additional details and additional algorithms in Chapter 5 of . Haftka. and . Gurdal’s. Elements of Structural Optimization. Optimization with constraints. Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. Collin . Bezrouk. 2-24-2015. Discussion Reference. Some of this material comes from . Spacecraft Trajectory Optimization. (Ch. 7) by Bruce Conway.. Optimization Problem Setup. Optimization problems require the following:. Clint Jeffery. University of Idaho. Outline. Preliminary thoughts. AIGPW Chapters. EvoGames. Papers. Conclusions. Preliminary Thoughts. ANN and related technologies are rare in commercial games. Behavior of ANN-based agents often perceived as bizarre or unrealistic. (x) = 0. h. i. (x) <= 0. Objective function. Equality constraints. Inequality constraints. Terminology. Feasible set. Degrees of freedom. Active constraint. classifications. Unconstrained v. constrained. :. Application to Compressed Sensing and . Other Inverse . Problems. M´ario. A. T. . Figueiredo. Robert . D. . Nowak. Stephen . J. Wright. Background. Previous Algorithms. Interior-point method. . Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. Diederik. P. . Kingma. . Jimmy Lei Ba. Presented by . Xinxin. . Zuo. 10/20/2017. Outline. What is Adam. The optimization algorithm. . Bias correction. Bounded . update. Relations with Other approaches. Yann . LeCun, Leon Bottou, . Yoshua Bengio and Patrick Haffner. 1998. . 1. Ofir. . Liba. Michael . Kotlyar. Deep learning seminar 2016/7. Outline. Introduction . Convolution neural network -. LeNet5. 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. 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats Nima Aghaee, Zebo Peng, and Petru Eles. Embedded Systems Laboratory (ESLAB). Linkoping University. 12th Swedish System-on-Chip Conference – May 2013. Outline. Introduction. Early life failures. Temperature gradient effects.