### 25 DAY MATHSFINAL REVISION COURSES

Course Content
1 – Introduction to Vectors
0/17
2 – 1D Horizontal Motion
0/17
3 – Motion due to Gravity
0/10
4 – Continuous Change (Calculus)
0/13
5 – Forces and Systems
0/18
6 – Introduction to Energy and Momentum
0/8
7 – Impacts and Collisions
0/8
8 – Uniform Circular Motion
0/11
9 – Difference Equations
0/11
10 – Introduction to Graph Theory
0/8
11 – Path Optimisation
0/7
12 – MST Optimisation
0/4
13 – Project Optimisation
0/11
Digital Lessons

## Overview

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The length of time that we recommend members of each team spend on this topic are as follows:

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Alpha – 12 days

Bravo – 9 days

Charlie – 7 days

Delta – 6 days

Echo – 4 days

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## Learning Objectives

Use Dijkstra’s Algorithm to find the shortest path(s) between two nodes

Apply Bellman’s Principle of Optimality to find the shortest path(s) between two nodes

State what is meant by a greedy algorithm

Explain why Dijkstra’s Algorithm is considered greedy

State what is meant by dynamic programming

State what is meant by a multi-stage network

Use Bellman’s Algorithm to find the shortest path(s) between two nodes of a multi-stage network

Explain why Bellman’s Algorithm is not considered greedy

Apply Bellman’s Algorithm using a table

Apply dynamic programming to routing problems

Apply dynamic programming to the allocation of resources

Apply dynamic programming to equipment replacement and maintenance

Apply dynamic programming to stock control

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## Keywords

Dijkstra’s Algorithm

Bellman’s Principle of Optimality

Dijkstra Key

greedy

Bellman’s Algorithm

dynamic programming

multi-stage network

stage

state

action

source

sink

forward recursion

backward recursion

optimal

destination

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