Simulation, Algorithm Analysis, and Pointers

Start Date: 09/15/2019

Course Type: Common Course

Course Link: https://www.coursera.org/learn/simulation-algorithm-analysis-pointers

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Course Syllabus

File IO and Automation
Simulation and Parallelization
Algorithm Analysis
Pointers

Deep Learning Specialization on Coursera

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Article Example
Simulation Project management simulation is simulation used for project management training and analysis. It is often used as training simulation for project managers. In other cases it is used for what-if analysis and for supporting decision-making in real projects. Frequently the simulation is conducted using software tools.
Competitive analysis (online algorithm) Competitive analysis is a method invented for analyzing online algorithms, in which the performance of an online algorithm (which must satisfy an unpredictable sequence of requests, completing each request without being able to see the future) is compared to the performance of an optimal "offline algorithm" that can view the sequence of requests in advance. An algorithm is "competitive" if its "competitive ratio"—the ratio between its performance and the offline algorithm's performance—is bounded. Unlike traditional worst-case analysis, where the performance of an algorithm is measured only for "hard" inputs, competitive analysis requires that an algorithm perform well both on hard and easy inputs, where "hard" and "easy" are defined by the performance of the optimal offline algorithm.
Cross impact analysis Advancements in simulation models continued into the 1980s. In 1980, Selwyn Enzer at the University of California incorporated Cross Impact Analysis into a simulation method known as Interax, The Delphi technique was combined with Cross Impact Analysis in 1984, and researchers at Texas A&M University used Cross Impact in a process called "EZ-IMPACT" that was based on Kane's algorithm from KSIM.
Simulation "Simulation in failure analysis" refers to simulation in which we create environment/conditions to identify the cause of equipment failure. This was the best and fastest method to identify the failure cause.
Competitive analysis (online algorithm) In competitive analysis, one imagines an "adversary" that deliberately chooses difficult data, to maximize the ratio of the cost of the algorithm being studied and some optimal algorithm. Adversaries range in power from the "oblivious adversary", which has no knowledge of the random choices made by the algorithm pitted against it, to the "adaptive adversary" that has full knowledge of how an algorithm works and its internal state at any point during its execution. Note that this distinction is only meaningful for randomized algorithms. For a deterministic algorithm, either adversary can simply compute what state that algorithm must have at any time in the future, and choose difficult data accordingly.
Barnes–Hut simulation The Barnes–Hut simulation (Josh Barnes and Piet Hut) is an approximation algorithm for performing an "n"-body simulation. It is notable for having order O("n" log "n") compared to a direct-sum algorithm which would be O("n").
Virginia Modeling, Analysis and Simulation Center Virginia Modeling, Analysis and Simulation Center (VMASC) is a multi-disciplinary research center of Old Dominion University. VMASC supports the University’s Modeling & Simulation (M&S) degree programs, offering M&S Bachelors, Masters and Ph.D. degrees to students across the Colleges of Engineering and Technology, Sciences, Education, and Business. Working with more than one hundred industry, government, and academic members, VMASC furthers the development and applications of modeling, simulation and visualization as enterprise decision-making tools to promote economic, business, and academic development.
Project management simulation Project management simulation is simulation used for project management training and analysis.
Algorithm "Simulation of an algorithm: computer (computor) language": Knuth advises the reader that "the best way to learn an algorithm is to try it . . . immediately take pen and paper and work through an example". But what about a simulation or execution of the real thing? The programmer must translate the algorithm into a language that the simulator/computer/computor can "effectively" execute. Stone gives an example of this: when computing the roots of a quadratic equation the computor must know how to take a square root. If they don't, then the algorithm, to be effective, must provide a set of rules for extracting a square root.
Pantelides algorithm Pantelides algorithm is implemented in several significant equation-based simulation programs such as gPROMS, Modelica and EMSO.
Simulation-based optimization Simulation-based optimization integrates optimization techniques into simulation analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate.
Simulation Some applications of ergonomic simulation in include analysis of solid waste collection, disaster management tasks, interactive gaming, automotive assembly line, virtual prototyping of rehabilitation aids, and aerospace product design. Ford engineers use ergonomics simulation software to perform virtual product design reviews. Using engineering data, the simulations assist evaluation of assembly ergonomics. The company uses Siemen's Jack and Jill ergonomics simulation software in improving worker safety and efficiency, without the need to build expensive prototypes.
Business simulation Business simulation is simulation used for business training, education or analysis. It can be scenario-based or numeric-based.
Strassen algorithm Strassen's algorithm is cache oblivious. Analysis of its cache behavior algorithm has shown it to incur
Algorithm engineering Some researchers describe algorithm engineering's methodology as a cycle consisting of algorithm design, analysis, implementation and experimental evaluation, joined by further aspects like machine models or realistic inputs.
Escape analysis In compiler optimization, escape analysis is a method for determining the dynamic scope of pointers - where in the program a pointer can be accessed. It is related to pointer analysis and shape analysis.
Task analysis environment modeling simulation Task Analysis, Environment Modeling, and Simulation (TAEMS or TÆMS) is a problem domain independent modeling language used to describe the task structures and the problem-solving activities of intelligent agents in a multi-agent environment.
Virginia Modeling, Analysis and Simulation Center The Virginia Modeling, Analysis, and Simulation Center (VMASC) began as an idea, a concept driven by need: the Joint Warfighting Center required short-courses and formal training of its staff and ODU determined it would take on that task. The concept became a plan, the plan became a Center; and all of this transpired over a three-year period from fall of 1994 to the summer of 1997. Things were set in motion in October 1994 with the establishment of the Joint Training, Analysis and Simulation Center as a part of the United States Atlantic Command (USACOM),
Swendsen–Wang algorithm The Swendsen–Wang algorithm is the first non-local algorithm for Monte Carlo simulation for large systems near criticality.
Wang and Landau algorithm The algorithm then performs a multicanonical ensemble simulation: a Metropolis-Hastings random walk in the phase space of the system with a probability distribution given by formula_10 and a probability of proposing a new state given by a probability distribution formula_11. A histogram formula_12 of visited energies is stored. Like in the Metropolis-Hastings algorithm, a proposal-acceptance step is performed, and consists in (see Metropolis–Hastings algorithm overview):