Below is a list of optimization techniques/topics and languages used for each assignment. Note that all assignments come with default trivial solution in python -- I don't count python unless I implemented something non-trivial in this assignment. Also, I started almost every assignment with implementing greedy or random solver, I don't count them either.

• Go (DP, BnB solver)
• Dynamic Programming (DP)
• Branch and Bound (BnB)

• Bash (wrapper)
• Go (CP solver)
• Constraint Programming (CP)
  • Minimum Remaining Variable (MRV)
  • Least Constraining Value (LCV)
  • Arc Consistency (AC3)

• Bash (wrapper)
• Go (LS solver)
• R (visualization)
• Python
  • visualization (igraph)
  • MIP problem generator, parser
  • scipy.kmeans
• Local Search (LS)
  • Simulated Annealing (SA)
  • Metropolis
  • 2-opt
  • Late Acceptance Hill Climbing
• Mixed Integer Programming (MIP)
  • external solver: SCIP
  • problem format: PIP
  • problem formulations: Miller-Tucker-Zemlin, subtour elimination

• Bash (wrapper)
• Python (MIP problem generator, parser)
• Mixed Integer Programming (MIP)
  • external solver: SCIP
  • problem format: PIP
  • problem formulations: SimpleModel, LectureModel

• Bash (wrapper)
• Go (LS solver, unit test)
• R (visualization)
• Python (MIP problem generator, parser)
• Local Search (LS)
  • Simulated Annealing (SA)
  • Metropolis
  • neighbour generation moves: 1. move customer from one route to another 2. swap two customers
• Mixed Integer Programming (MIP)
  • external solver: SCIP
  • problem format: PIP
  • problem formulations: AssignCustomersModel (similar to WLP), OrderCustomersModel (similar to TSP)

• Python (nqueens CP solver back from university times)
• Octave (magic square)
• Constraint Programming (CP)
  • Minimum Remaining Variable (MRV)
  • Least Constraining Value (LCV)