Mathematical Optimization

When to use

• Formulating a real-world problem as a mathematical optimization model
• Choosing between LP, MIP, convex, CSP, or combinatorial approaches
• Selecting the right gradient descent variant for a machine learning or fitting problem
• Solving scheduling, assignment, routing, or bin packing problems
• Applying SAT/SMT solvers or constraint programming for logical constraint problems
• Evaluating solver options (PuLP, CVXPY, OR-Tools, Gurobi, Z3)

Core principles

1. Formulate before you code — identify variables, objective, and constraints explicitly before touching a solver
2. Tight LP relaxation = faster MIP — every improvement to the relaxation bound shrinks the branch-and-bound tree
3. Convexity is the dividing line — convex problems are reliably solvable; non-convex require restarts and heuristics
4. Adam is not always the answer — L-BFGS beats Adam on smooth well-conditioned problems with moderate dimension
5. CP-SAT over custom backtracking — Google OR-Tools CP-SAT handles constraint propagation and search orders better than hand-rolled solvers

Reference Files

• references/linear-and-integer-programming.md — LP formulation, simplex vs interior point, duality and shadow prices, MIP branch-and-bound, Big-M method, symmetry breaking, solver library options
• references/convex-and-gradient.md — convexity verification (Hessian PSD), gradient descent variants (SGD, Adam, L-BFGS), Newton/conjugate gradient second-order methods, CVXPY/SciPy/JAX tooling
• references/constraint-and-combinatorial.md — CSP formulation, backtracking + constraint propagation, SAT/SMT solvers (Z3, CDCL), VRP/scheduling/assignment/bin packing patterns, problem classification guide