Model

Mathematical Programming

Create an optimization model for LP, MIP, or QP. Methods: m.addVar(name, lb, ub) — continuous variable m.addVar(name, 'binary') — binary (0/1) variable m.addVar(name, lb, ub, 'integer') — integer variable m.addVars(n, name, ...) — 1D array of variables m.addVars([r,c], name, ...) — 2D array of variables m.setObjective(expr) — set linear/quadratic objective m.addConstr(expr, 'name') — add constraint (<=, >=, ==) result = m.solve() — solve and return result Result fields: status, objVal, vars, duals, exitflag, solveTime

m = Model('minimize')

m = Model('maximize')

% LP: Product mix
m = Model('maximize')
x = m.addVar('tables', 0, Inf)
y = m.addVar('chairs', 0, Inf)
m.setObjective(70*x + 50*y)
m.addConstr(4*x + 3*y <= 240, 'wood')
result = m.solve()

% MIP: Knapsack with addVars
m = Model('maximize')
items = m.addVars(3, 'x', 'binary')
val = [10, 8, 3]
m.setObjective(val * items)
m.addConstr([5, 4, 1] * items <= 10)
result = m.solve()