M1 Artificial Intelligence · semester 7 · LP, IP, CP

Operations Research

Introduction to Artificial Intelligence, part 2: linear programming, duality, integer programming and branch and bound, then constraint programming with Choco. Every Caseine exercise has a folder here, statement included.

Two halves, one grader

OPL 26 models, solved with CPLEX Production planning, blending, scheduling and network flow, formulated as linear and mixed-integer programs and checked on Caseine's own grader. Choco 11 models, solved with pychoco Puzzles and combinatorial problems — magic squares, N-queens, car sequencing — modelled as constraint programs in Python.

How the work is recorded

Each exercise that gets solved carries a steps page: what it asks, and once it is solved, what was tried, what was rejected, and the reference that settled it. A choice without a reference is a guess, so the trail is written down where the code can be read beside it.

Reading it

Source The folder on GitHub Both halves, each with its own exercise folders and completed models once the work is done. README The course README The full tables of exercises across both halves, what each is solved with, and what is provided going in.

The exercise statements, and the starter models and data Caseine distributes with them, are not redistributed here. Each exercise keeps its handout on disk, out of the repository; what is committed is my own work.