Solving Problems with Numerical Methods
This course focuses on conceptually understanding and implementing numerical techniques to solve mathematical problems. Many problems in the real world are hard, or impossible, to solve analytically but easy to solve numerically.
What you'll learn
The growth in computing power means that problems that were hard to solve earlier can now be tackled using numerical techniques. These are algorithms that seek to find numerical approximations to mathematical problems rather than use symbolic manipulation i.e. fit a formula. Symbolic manipulation is often very hard and may not always be tractable. Numerical analysis, on the other hand, allows us to give approximate answers to hard problems such as weather prediction, computing the trajectory of a spacecraft, setting prices for goods in real-time and in many other use cases. In this course, Solving Problems with Numerical Methods we will explore a wide variety of numerical techniques for different kinds of problems and learn how we can apply these techniques using the R programming language. First, you will learn how numerical methods are different from analytical methods and why it is important to be able to solve problems using numerical procedures. You will understand and work with direct and iterative numerical techniques to solve a system of linear equations and perform interpolation and extrapolation using a variety of different methods. Next, you will discover how graphs can be represented and the applications of graph algorithms in the real world. You will then move on to local search techniques to solve the N-queens problem. You will study variants of classic local search such as stochastic local search algorithms, simulated annealing and threshold accepting algorithms. These techniques allow locally bad moves to avoid getting stuck in local optima. Finally, you will explore how to formulate a linear programming problem by setting up your objective, constraints and decision variables and them implement a solution using R utilities. You will round off this course by understanding and implementing differentiation and integration using R programming. When you’re finished with this course, you will have the skills and knowledge to apply a variety of numerical procedures to solve mathematical problems using the R programming language.
Table of contents
- Version Check 0m
- Prerequisites and Course Outline 3m
- Introducing Numerical Methods 4m
- Direct and Iterative Numerical Methods 5m
- Numerical Instability and Errors 4m
- Interpolation and Extrapolation 3m
- Constant, Linear, Polynomial, and Spline Interpolation 5m
- System of Linear Equations 3m
- Gaussian Elimination and Jacobi Method 7m
- Demo: Linear and Constant Interpolation 7m
- Demo: Linear and Polynomial Extrapolation 6m
- Demo: Interpolation Using Spline and Smoothing Techniques 2m
- Demo: Iterative Techniques - Jacobi Method to Solve Linear Equations 5m
- Demo: Direct Techniques - Gaussian Elimination to Solve Linear Equations 3m
- Demo: Discretizing Continuous Data 6m
- Demo: Rounding Errors and Approximation Errors 4m
- Demo: Approximating Derivate Calculations 5m
- Demo: Euler's Method and Numerical Instability 9m
- Graphs and Graph Applications 5m
- Directed and Undirected Graphs 4m
- Connected and Unconnected Graphs 2m
- Graph Representations 5m
- Shortest Path Algorithms 5m
- Demo: Defining and Configuring Graphs 5m
- Demo: Specifying Attributes for Edges and Vertices 3m
- Demo: Exploring Different Kinds of Graphs 4m
- Demo: Calculating Shortest Distances 6m
- Demo: Calculating Shortest Paths 3m
- Solutions Approaches to N-Queens 7m
- Introducing Local Search Algorithms 5m
- Simulated Annealing and Threshold Accepting 3m
- Demo: 8 Queens - Calculating Number of Attacks 9m
- Demo: 8 Queens - Generating Candidate Solutions 2m
- Demo: 8 Queens - Solution Using Local Search Techniques 4m
- Objective Constraints and Decision Variables 6m
- Wyndor Glass: Framing the Optimization Problem 4m
- Setting up the Optimization as a Linear Programming Problem 2m
- Solving the Linear Programming Problem Graphically 3m
- Demo: Solving Optimization Problems Using Linear Programming 4m
- Demo: Linear Programming to Solve the Wyndor Glass Problem 3m
- Modeling Population Growth 4m
- Interpreting Derivatives 6m
- Verhulst's Equation for Population Growth 5m
- Understanding Integration 4m
- Demo: Calculating Derivatives 4m
- Demo: Calculating Velocity and Acceleration Using Derivatives 3m
- Demo: Performing Integration 5m
- Demo: Using the ODE Solver to Solve Differential Equations 6m
- Demo: Solving Verhulst's Equation 5m
- Summary and Further Study 1m