Course Information


The course meets in D+ block, from 10:30-11:45 AM on Tuesdays and Thursdays, in Room BP-6. My office hours are from 9:30 - 11:30 AM on Mondays and 1:30 - 2:30 PM on Wednesdays, in Room BP-212.

There are no required nor recommended texts for this class

The programming for this course can be done in any language that you choose. Matlab is probably the easiest to pick up if you are not familiar with any other. All Tufts students have access to Matlab in the ITS Computing Center @ Eaton Hall. You can purchase the student edition from the bookstore for just over $100 or, slightly cheaper, direct from the Mathworks here. A free alternative to Matlab is Octave. Other possibilities include using python with numpy/scipy/matplotlib. If you would like to use another option, please discuss this with me.

Matlab Resources
Approximate Schedule

  • 9/3: Introduction
  • 9/5: One-dimensional optimization, sensitivity analysis
  • 9/10: Multi-dimensional optimization, Lagrange Multipliers, shadow prices; HW1 distributed
  • 9/12: Intro to and plotting in Matlab
  • 9/17: Optimization using Newton's Method and variants; HW2 distributed; files bisection.m, secant.m, newton.m, halley.m
  • 9/19: Types of optimization, linear programming; HW1 due
  • 9/24: Linear programming and fair division; HW3 distributed; file cake_division.m
  • 9/26: Network flows and linear programming; HW2 due
  • 10/1: The simplex algorithm; Midterm project 1 distributed
  • 10/3: Duality and complementary slackness; HW3 due
  • 10/8: Dual prices, integer programming
  • 10/10: Branch and bound algorithms; HW4 distributed
  • 10/17: Graph models, max flow and min cut; Midterm Project 1 due
  • 10/22: Bipartite graphs, matchings, selection problems; HW5 distributed
  • 10/24: Scheduling problems, critical path method, Gantt charts; HW4 due
  • 10/29: Finite-state machines, transition diagrams; HW6 distributed
  • 10/31: Iterations matrices, eigenvectors, power iterations; HW5 due
  • 11/5: Stochastic Matrices, Markov Chains; Midterm Project 2 distributed
  • 11/7: Intro to statistics, central limit theorem; HW6 due
  • 11/12: Monte-Carlo Integration, pseudorandom numbers
  • 11/14: Monte-Carlo Simulation
  • 11/19: Binomial and Poisson distributions; Midterm Project 2 due, HW7 distributed, strategy1.m, strategy2.m.
  • 11/21: Recurrence Relations and Generating functions
  • 11/26: Logistic Maps and Predator-Prey models; HW8 distributed
  • 12/3: Logistic Functions and Lotka-Volterra models; HW7 due
  • 12/5: Conservation laws and fluid dynamics; HW8 due
  • 12/12: No class, but final projects due at 3:30 PM