PPT-CS b553 : A lgorithms for Optimization and Learning

Bayesian Networks agenda Probabilistic inference queries Topdown inference Variable elimination Probability Queries Given some probabilistic model over variables X Find distribution over . Univariate. optimization. x. f. (x). Key Ideas. Critical points. Direct methods. Exhaustive search. Golden section search. Root finding algorithms. Bisection. [More next time]. Local vs. global optimization. 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,. : Algorithms . for Optimization and Learning. Global optimization. 1. Agenda: . Global Optimization. Local search, optimization. Branch and bound search. Online . search. 3. Global . Optimization. min . (pages from . Bendsoe. and Sigmund and Section . 6.5). Looks for the connectivity of the structure. How many holes.. Optimum design of bar in tension, loaded on right side. Structural Optimization categories. Jonathan Hollingshead. Terms used in this presentation. Web page or page – a single document . on the Internet, typically with a single topic. Web site or site – a collection of individual web pages interconnected by hyperlinks. Linear programming, quadratic programming, sequential quadratic programming. Key ideas. Linear programming. Simplex method. Mixed-integer linear programming. Quadratic programming. Applications. Radiosurgery. Learning. Structure . Learning. Agenda. Learning probability distributions from . example data. To what extent can Bayes net structure be learned?. Constraint methods (inferring conditional independence). L. . T. . Biegler. Joint work with Alex Dowling, . Ravi . Kamath. , Ignacio Grossmann. June, 2014. Overview. Introduction. Process optimization . Formulation and solution strategies. Bilevel. Optimization . Kadin Tseng. Boston University. Scientific Computing and Visualization. Outline. Introduction. Timing. Example Code. Profiling. Cache. Tuning. Parallel Performance. Code Tuning and Optimization. 2. Introduction. P. rovably Optimal . I. mplementations with . R. esiliency and . E. fficiency. Elad. . Alon. , . Krste Asanovic (Director). ,. Jonathan . Bachrach. , Jim . Demmel. , Armando Fox, Kurt . Keutzer. , . 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,. Additive manufacturing (AM) is expanding the range of designable geometries, but to exploit this evolving design space new methods are required to find optimum solutions. Finite element based topology PI: Maksim . Rakitin. , . Co-PIs: Mikhail . Fedurin. , Yonghua Du. Brookhaven National Laboratory. LDRD#22-031 PS/EPS (“Received”). January 20, 2022. Experiment Goals at ATF:. Collect e-beam tunning data (PS set/read, BPM read ). Michael Kantor. CEO and Founder . Promotion Optimization Institute (POI). First Name. Last Name. Company. Title. Denny. Belcastro. Kimberly-Clark. VP Industry Affairs. Pam. Brown. Del Monte. Director, IT Governance & PMO.