HUNTERTUTORING

Engineering optimization

Graduate · Engineering

Syllabus focus

Standard syllabus · STEM / applied

Pricing

Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.

Topics typically covered

Standard syllabus

Linear and nonlinear programming

  • Optimization problem formulation and convexity
  • Linear programming: simplex method and duality
  • Sensitivity analysis and shadow prices
  • Integer programming and branch-and-bound
  • Unconstrained nonlinear optimization algorithms
  • Gradient descent, Newton, and quasi-Newton methods
  • Constrained optimization: KKT conditions
  • Penalty and barrier methods
  • Sequential quadratic programming (SQP)
  • Global optimization heuristics overview

Specialized engineering formulations

  • Least squares and regression as optimization
  • Multi-objective optimization and Pareto fronts
  • Dynamic programming and optimal control link
  • Stochastic programming and chance constraints intro
  • Robust optimization under uncertainty
  • Topology optimization SIMP method
  • Shape optimization and adjoint methods
  • Scheduling and network flow problems
  • Engineering design optimization case studies
  • Convex relaxations for nonconvex problems

STEM / applied

Computation and software

  • MATLAB Optimization Toolbox and CVX
  • Python: SciPy, PuLP, and Pyomo
  • GAMS and AMPL modeling languages survey
  • Large-scale solvers: IPOPT, Gurobi licensing
  • Parallel and decomposition methods
  • Simulation-based optimization and surrogate models
  • Machine learning hyperparameter tuning as optimization
  • Real-time optimization in process control
  • Benchmark problems and reproducibility
  • Thesis optimization model development

Applications across disciplines

  • Structural weight minimization with constraints
  • Aerospace vehicle trajectory optimization
  • Supply chain and logistics network design
  • Energy unit commitment problems
  • Parameter estimation in biochemical models
  • Portfolio optimization analogies in engineering risk
  • Autonomous vehicle path planning objectives
  • Multi-disciplinary design optimization (MDO)
  • Industry guest cases from operations research
  • Qualifying exam optimization preparation

Notes

Topics reflect common engineering syllabi at US colleges and universities. Exact order, depth, and applied emphasis vary by institution, department, and instructor.