AI Bootcamp: Mathematical Optimization

Mathematical Optimization, part of our AI Bootcamp Programme, is a specialized, intensive one-week course designed to equip participants with a solid understanding of optimization concepts through a learn-by-doing approach. Participants will explore various optimization problems, covering everything from basic to complex formulations, including linear, nonlinear, and mixed-integer optimization with constraints. The course focuses on practical applications in diverse fields such as finance, management, and supply chain, addressing key issues like manufacturing, profit maximization, and resource allocation.

By integrating theoretical knowledge with hands-on experience using Python, attendees will gain essential skills and industry best practices that can be applied regardless of the software they use. By the end of the program, participants will be well-prepared to tackle real-world challenges and effectively implement optimization techniques in their professional roles across various domains.

Who it is for

This course is designed for business professionals seeking a short specialization in mathematical optimization, focused on solving their existing challenges in the most efficient manner. Participants should have prior experience in their respective fields and a foundational understanding of the problems they wish to address. Throughout the course, we will provide comprehensive know-how, including exact steps and methodologies, to empower attendees to transform real-world issues into mathematical optimization models and achieve optimal solutions effectively.

Syllabus

introduction to optimization
optimization problem formulation
convex optimization
algorithms complexity
linear programming
quadratic programming
mixed-integer linear programming
nonlinear optimization
heuristic methods to optimization
demonstration case study
hands-on case study

What will you learn

to understand the basic mathematical optimization principles
to formulate and program optimization problems
to select optimization algorithm
to solve optimization problems in Python
apply optimization techniques to solve real world problems

Prerequisites

basic mathematical skills (linear algebra is a plus)
basic level of Python proficiency
exposure to numerical computing, optimization, and application fields is helpful but not required

Pricing

1.200 EUR per person (w/o VAT) includes:
- one-week full-day course
- course materials (code, datasets, presentations)
- lunch
- shareable certificate

Course schedule

Case studies

Demonstration

The Demonstration Case Study is based on the realistic dataset and covers typical real-world optimization problems. Participants will be guided through essential steps, including mathematical modeling, problem formulation, constraint definition, solver selection, and solution analysis. They will tackle diverse optimization problems in Python, such as linear programming, quadratic programming, mixed-integer linear programming, and nonlinear optimization. Participants will learn techniques for addressing specific problem characteristics and discuss the strengths and limitations of various algorithms to gain practical insights into their effectiveness and performance.

Hands-on

The hands-on session will be conducted through a private Kaggle competition aimed at optimizing the energy trading strategy of a large business skyscraper building with renewable energy systems and energy storage capacity. Scenario includes simulated consumption data from a large scale office building with included production of a rooftop photovoltaic power plant, basement battery storage capacity, and relevant energy market data, including prices and ancillary services. Participants will formulate and solve the optimization problem under constraints like technical operation limits and market conditions to minimize the cost of daily building operation. During the entire process of designing the decision support system, participants will have full support of the course tutors.

Contact

Address	Laboratory for Renewable Energy Systems (LARES) Faculty of Electrical Engineering and Computing Unska 3 HR-10000 Zagreb Croatia
Phone	+385 1 6129 893
Email	hrvoje.novak@fer.hr