Basic Modeling for Discrete Optimization

CUHK introduces discrete optimization by expressing decisions and restrictions in MiniZinc. Sets, functions and alternative formulations support problems such as allocation or scheduling. It suits learners who want to connect mathematical constraints with an executable model and learn why a correct formulation matters before interpreting a solver’s output.

What you will learn

• Represent discrete decisions and constraints in MiniZinc.
• Use sets and functions in optimization models.
• Compare different formulations of the same problem.

Who this suits

Intermediate learners willing to install and use MiniZinc and translate a problem into variables and constraints; review the provider’s setup instructions before starting.

Before you enroll

Work through examples with a written argument rather than relying on an answer alone. Check definitions, quantifiers and boundary cases, and distinguish a model’s assumptions from the situation it represents. These courses take different routes through discrete mathematics; compare their preparation requirements and topic coverage before choosing a starting point.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Model a small fictional seating problem with stated constraints. Check the output manually, then alter one assumption and explain how the feasible solutions change.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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