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Random Search Algorithms. Simulated Annealing

Random Search Algorithms. Simulated Annealing

Random Search Algorithms. Simulated Annealing. Motivation Simple Random Search Algorithms Simulated Annealing Algorithms. Motivation. Global optimization: Identify the global optimum of a function : f(x*)>=f(x), for all x

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1.03k views • 29 slides
The Future (and Past) of Quantum Lower Bounds by Polynomials

The Future (and Past) of Quantum Lower Bounds by Polynomials

The Future (and Past) of Quantum Lower Bounds by Polynomials. Scott Aaronson UC Berkeley. Outline. The quantum query model Quantum lower bounds for collision and set comparison problems Open problems. Quantum Query Model. Count only number of queries, not number of computational steps

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293 views • 29 slides
Today

Today

Today. Introduction to MCMC Particle filters and MCMC A simple example of particle filters: ellipse tracking. Introduction to MCMC. Sampling technique Non-standard distributions (hard to sample) High dimensional spaces Origins in statistical physics in 1940s

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353 views • 22 slides
The Transition from Welfare to Work and the Role of Potential Labor Income

The Transition from Welfare to Work and the Role of Potential Labor Income

The Transition from Welfare to Work and the Role of Potential Labor Income. Hilmar Schneider (IZA, DIW Berlin) Arne Uhlendorff (DIW Berlin, IZA). The Transition from Welfare to Work and the Role of Potential Labour Income. Introduction Literature Social assistance in Germany

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326 views • 17 slides
Configurational Bias Methods for Monte Carlo Simulation

Configurational Bias Methods for Monte Carlo Simulation

Configurational Bias Methods for Monte Carlo Simulation. Lin Chen. Advisor: Dr. David Smith. 2007 Fall PIP discussion. Metropolis Monte Carlo scheme. Insertion trial. Overlap large ?U. Cavity, small ?U. Acceptance Probability = Aexp(-( ? U-?)/k b T). ? chemical potential of substance.

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436 views • 17 slides
Objective

Objective

Inverse Modeling of Hydrologic Parameters using Surface Flux and Runoff Observations in the Community Land Model (CLM) . Objective Explore the feasibility of calibrating hydrologic parameters using surface flux and runoff observations to improve the accuracy of CLM Approach

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106 views • 1 slides

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