On Education Discrete Optimization Data Science Heuristic & Metaheuristic - CouponED

What is optimization Some real-life situations where we need to optimize an objective The mathematical formalism of optimization How discrete optimization (Combinatorics) differs from continuous optimization Different approaches to solve a Combinatorics problem, including-- The simplest, perfect but slow'Brute Force' method. One of the fastest and practicable'Greedy' heuristic.A look-ahead mechanism to refine the greedy approach. Travelling Salesman Problem Other generic problems in discrete optimization, like the Knapsack Problem How metaheuristic approaches compare to heuristic solutions The nature-inspired class of metaheuristic approaches Ant Colony Optimization: its basis, modus operandi, algorithm and flow chart The R library to implement Ant Colony Optimization and other heuristic solutions Examples of Travelling Salesman Problems solved through different approaches ESSENTIAL: A moderate knowledge of Mathematics (High School level) BOOSTER: Familiarity with some programming language (preferably R) BOOSTER: Interest in solving puzzles and games involving logic BOOSTER: Basic knowhow on what Data Science is about Discrete Optimization is something all of us use in our daily activities when say, we order at a restaurant, decide which subject to study, take up a new activity… or look for a change. It comprises of choosing between alternatives that best suit some objective we have in mind. When such things are formalized, i.e. the objective and the ability of each choice to fulfill that objective are quantified, we get a mathematical expression of the problem we would optimize.