07-380 HW2 has three parts. The programming assignment has you write PDDL for a pancake-cooking robot, solve it optimally with unified-planning and Fast Downward, then implement RRT and RRT* in rrt.py (Q2–Q7). The written part covers GraphPlan, one LP modeling problem, and two LP graphing problems. A Gradescope online component is CMU-only. This guide covers structure, prerequisites, and running the local autograder; it contains no solutions.
HW3 has three parts. The programming part has you build an LP solver by vertex enumeration, stack branch and bound on top of it for integer programs, and formulate three word problems. The written part covers integer programming by hand, the ethics of Amazon's delivery routing, PCA via SVD, and a proof that a Laplace prior equals L1. It is due 10/1, so this guide explains structure and concepts only, with no solutions.
07-380 Lec5 turns the Diet Problem from words into min cᵀx s.t. Ax ⪯ b, then draws it: each constraint is a half-plane, the cost is a direction, and cost contours are perpendicular to c. Push a contour in the −c direction until it last touches the feasible region and you always hit a vertex, so solvers only need the intersections of constraint boundaries. Vertex enumeration checks them all; simplex walks greedily from one vertex to a better neighbor.
07-380 Lec6 adds one constraint to an LP, x ∈ ℤᴺ, and the vertex solution may no longer be an integer. Searching the integer points near the LP solution is not guaranteed to work either. The fix: drop the integer constraint (relaxation) to get an LP lower bound, split on a fractional coordinate into xᵢ ≤ floor and xᵢ ≥ ceil, and keep every subproblem in a priority queue ordered by LP objective. The first all-integer solution popped is optimal.
The finale recaps the CS161 toolbox and points toward LP duality, Reed–Solomon coding, and ML-assisted algorithms. Officially, this lecture has slides but no notes.