Start Date: 05/31/2020
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/simulation-algorithm-analysis-pointers
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.This course is the fourth and final course in the specialization exploring both computational thinking and beginning C programming. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components. Most people have a better understanding of what beginning C programming means! This course assumes you have the prerequisite knowledge from the previous three courses in the specialization. You should make sure you have that knowledge, either by taking those previous courses or from personal experience, before tackling this course. The required prerequisite knowledge is listed below. Prerequisite computational thinking knowledge: Algorithms and procedures; data collection, analysis, and representation; abstraction; and problem decomposition Prerequisite C knowledge: Data types, variables, constants; STEM computations; selection; iteration (looping); arrays; strings; and functions Throughout this course the computational thinking topics you'll explore are: automation, simulation, parallelization, and algorithm analysis.For the programming topics, you'll continue building on your C knowledge by implementing file input and output in your programs and by exploring pointers in more depth. Module 1: Learn how to read, write, and append to files. Explore automation Module 2: Discover the benefits of simulation and parallelization Module 3: Learn how to perform algorithm analysis to quantify algorithm complexity Module 4: Explore how to use pointers in more depth
File IO and Automation
Simulation and Parallelization
Algorithm Analysis
Pointers
Simulation, Algorithm Analysis, and Pointers In this first course in the specialization, we will learn the basic algorithmic analysis and simulation techniques needed to be able to solve a variety of big problems in computer programming. We will start by learning the basics of basic optimization and simulation, then move on to more advanced topics such as simulation of algorithms that are used in industry or in science, formal verification of programs, and programming compilation. We will also cover some basic algorithmic issues such as average-case evaluation and algorithmic tradeoffs. We will discuss some of the most important programming languages, such as C, C++, and Java, and also some of the most common data structures, such as arrays and pointers. We will also look at basic optimization techniques, such as average-case evaluation, and formal verification of programs. We will introduce the typical use cases for each of these topics. Learning Outcomes By completing this course, you will be able to write robust programs, verify programs, and solve large programs with high quality. You will also be able to choose the best programming language to use for this important programming task. Suggested Reading There are no specific required texts for this course. However, the following books are helpful background reading for the course: *Linearity: Principles and Applications in Computer Science, Volume 1, Chapter 12 *Mathematics of Circular Arithmetic, Chapter 6 * Linearity: Problems in Compiling, Chapter 5Introduction to Simulating
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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): |