name	linearization-techniques
description	When the user wants to turn nonlinear terms — variable products, absolute values, min/max, piecewise-linear functions, logical implications, or fractional objectives — into mixed-integer linear constraints, or needs tight big-M values. Also use when the user mentions "linearize," "big-M," "bilinear," "piecewise linear," "indicator constraint," "product of variables," or "McCormick," or when a quadratic model must run on a MILP solver. For overall MILP construction, see milp-modeling-gurobi; for formulation strength and cuts, see integer-programming-techniques.

name

linearization-techniques

description

When the user wants to turn nonlinear terms — variable products, absolute values, min/max, piecewise-linear functions, logical implications, or fractional objectives — into mixed-integer linear constraints, or needs tight big-M values. Also use when the user mentions "linearize," "big-M," "bilinear," "piecewise linear," "indicator constraint," "product of variables," or "McCormick," or when a quadratic model must run on a MILP solver. For overall MILP construction, see milp-modeling-gurobi; for formulation strength and cuts, see integer-programming-techniques.

Linearization Techniques

You are an expert in reformulating nonlinear and logical model constructs as mixed-integer linear programs. This skill covers exact linearization of variable products, absolute values, min/max terms, piecewise-linear functions, logical implications, and fractional objectives, plus the discipline of choosing tight big-M constants. Use the framework below to classify each nonlinear term, pick the sharpest valid reformulation, and verify the result.

Initial Assessment

Establish these facts before proposing any reformulation:

Inventory the nonlinear terms. List every product, absolute value, min/max, ratio, piecewise function, and if-then condition. The right technique differs per term type.
Variable domains in each product. Binary times binary and binary times continuous have exact linearizations. Continuous times continuous does not — only relaxations (McCormick) or piecewise approximations. Confirm which case you are in before promising exactness.
Finite bounds. Almost every technique here needs finite lower/upper bounds on the continuous variables involved. Ask where bounds come from: physical limits, capacity data, or bound-propagation. If a variable is genuinely unbounded, fix that first.
Convex use or nonconvex use. A term like |w| or max(w1, w2) needs no binaries when the optimization direction already pushes it the right way (epigraph use). It needs binaries when used in the opposite direction. Identify the direction before adding integer variables.
Where the term sits. Objective-only nonlinearity sometimes allows lighter treatment (epigraph, Dinkelbach) than nonlinearity inside constraints.
Solver and license. Gurobi, CPLEX, and SCIP accept indicator constraints, SOS sets, and native PWL/general constraints; a pure-LP or open-source pipeline may force manual big-M. Gurobi can also solve bilinear models directly with Params.NonConvex = 2 — sometimes the honest answer is "do not hand-linearize."
Problem size after reformulation. Linearizing all products in a quadratic model with n² binaries creates O(n⁴) variables. Estimate the blow-up; pick a compact scheme (e.g. Kaufman–Broeckx) if the full one will not fit in memory.
Accuracy requirement. Piecewise-linear approximation of a smooth function trades breakpoint count against error. Get a tolerance before choosing the grid.
Numerical scale. Note the ratio between the largest and smallest constraint coefficients you are about to create. Big-M values that push this ratio past ~1e6 will cause solver trouble; plan bound tightening up front.

Linearization Catalog

Quick reference. "Exact" means the reformulation describes the same feasible set; "relaxation" means it only over-approximates it.

Nonlinear term	Reformulation	Extra vars / constrs	Exact?
z = x·y, x,y binary	Fortet inequalities	1 cont. / 3	exact, hull-sharp
z = x·w, x binary, w ∈ [L,U]	4-inequality envelope	1 cont. / 4	exact, hull-sharp
z = v·w, both continuous	McCormick envelope	1 cont. / 4	relaxation only
t = \|w\|, convex use	epigraph t ≥ ±w	1 cont. / 2	exact in epigraph use
t = \|w\|, nonconvex use	sign split + 1 binary	3 / 4	exact
t = max(w₁..w_m), convex use	t ≥ w_i	1 cont. / m	exact in epigraph use
t = max(w₁..w_m), nonconvex use	selector binaries	m bin. / 2m+1	exact
x=1 ⟹ a·w ≤ b	big-M or indicator	0 / 1	exact given valid M
piecewise-linear f(w)	SOS2 / incremental / multiple choice	see PWL table	exact on breakpoints
max (c·x+α)/(d·x+β), LP	Charnes–Cooper scaling	1 cont. / 1	exact
max (p·x+p₀)/(q·x+q₀), MIP	Dinkelbach iterations	none (sequence of MIPs)	exact at convergence

Products of variables

Binary × binary. For x, y ∈ {0,1}, z = xy is enforced by

$$z \le x,\qquad z \le y,\qquad z \ge x + y - 1,\qquad z \ge 0.$$

z may stay continuous; integrality of x, y forces it. These inequalities describe the convex hull of the five feasible points, so nothing tighter exists (Fortet 1960; Glover & Woolsey 1974, converting 0-1 polynomial programs to 0-1 linear programs). For a product of three or more binaries, chain pairwise or use the single-z generalization z ≥ Σx_i − (k−1), z ≤ x_i.

Binary × continuous. For x ∈ {0,1}, w ∈ [L, U], z = xw is enforced by

$$z \le Ux,\qquad z \ge Lx,\qquad z \le w - L(1-x),\qquad z \ge w - U(1-x).$$

Also hull-sharp (Glover 1975, improved linear integer formulations of nonlinear integer problems). Tight bounds L, U matter: they are the big-Ms of this reformulation.

Continuous × continuous. For v ∈ [Lv, Uv], w ∈ [Lw, Uw], the McCormick envelope (McCormick 1976, computability of global solutions to factorable nonconvex programs)

$$z \ge L_v w + L_w v - L_v L_w,\quad z \ge U_v w + U_w v - U_v U_w,$$ $$z \le U_v w + L_w v - U_v L_w,\quad z \le L_v w + U_w v - L_v U_w$$

is only a relaxation. Exactness requires spatial branching (let the solver do it via NonConvex = 2) or piecewise McCormick (see Advanced Techniques).

Absolute value, minimum, maximum

For t = |w|, w ∈ [L, U] with L < 0 < U: in epigraph use (minimizing t, or t on the small side of ≤ constraints), t ≥ w and t ≥ −w suffice. Otherwise split w = w⁺ − w⁻ with w⁺ ≤ U·b, w⁻ ≤ −L·(1−b), b binary, and set t = w⁺ + w⁻. The binary forbids simultaneous positive parts, which is what the epigraph version cannot do.

For t = max_i w_i: epigraph use needs only t ≥ w_i. Exact encoding adds selector binaries:

$$t \ge w_i ;\forall i,\qquad t \le w_i + \big(\max_j U_j - L_i\big)(1 - b_i);\forall i,\qquad \sum_i b_i = 1.$$

min(·) is symmetric (flip signs). The coefficient max_j U_j − L_i is again a big-M built from bounds — tighten the bounds and the formulation tightens with them.

Logical implications

x = 1 ⟹ a·w ≤ b becomes a·w ≤ b + M(1 − x) with M ≥ max{a·w} − b over the feasible region. The reverse direction a·w < b ⟹ x = 1 is modeled through the contrapositive x = 0 ⟹ a·w ≥ b; a strict inequality needs an explicit tolerance ε on one side, chosen larger than the solver's feasibility tolerance. Indicator constraints (addGenConstrIndicator) express the same implication without any M; the solver converts internally, often deriving better constants than a careless user would. Disjunctions (w ∈ P₁ ∨ w ∈ P₂) admit either big-M per disjunct or the tighter but larger convex-hull extended formulation (Balas 1998, disjunctive programming).

Piecewise-linear functions

Given breakpoints (x₀,y₀),…,(x_K,y_K), the main exact MILP encodings:

Method	Binaries	Idea	Use when
Convex epigraph	0	t ≥ slope_k·w + intercept_k	f convex and minimized (or concave and maximized)
Lambda + SOS2	0 (SOS2 set)	convex combination of breakpoints	solver branches well on SOS; no bound on K
Incremental (delta)	K−1	fill segments left to right	sequential/filling structure; couples well with fixed charges
Multiple choice	K	one binary per segment, disaggregated w	strong LP relaxation wanted; locally ideal
Logarithmic	⌈log₂K⌉	Gray-code branching on segments	many breakpoints (K ≳ 16)

Croxton, Gendron & Magnanti (2003) show the LP relaxations of the standard exact encodings coincide with the lower convex envelope; Vielma, Ahmed & Nemhauser (2010) compare them computationally — multiple choice and logarithmic usually win on hard instances.

Fractional objectives

Maximizing (c·x + α)/(d·x + β) over a polyhedron with positive denominator: Charnes & Cooper (1962) substitute t = 1/(d·x + β), y = tx, giving the LP max c·y + αt subject to Ay − bt ≤ 0, d·y + βt = 1, y, t ≥ 0; recover x = y/t. With integer variables the substitution breaks (y = tx is bilinear), so use Dinkelbach (1967): repeatedly solve the parametric problem max p·x + p₀ − λ(q·x + q₀) and update λ to the incumbent ratio; the root of F(λ) is the optimal ratio, and convergence is superlinear. A third option for 0-1 fractional programs: multiply out and linearize the binary-times-continuous products with the Glover envelope above.

Choosing tight big-M values

Rules that prevent most big-M damage:

Derive every M from bounds, never invent one. M = (interval-arithmetic max of LHS) − RHS is the smallest generally valid constant. Camm, Raturi & Tsubakitani (1990), "cutting big M down to size," is the classic statement.
One M per constraint, not one global M. Per-row constants are almost always smaller.
Spend an LP to save a tree. When interval arithmetic is loose, solve the auxiliary bounding LP max{a·w : relaxed constraints} for a valid and tighter M.
Watch IntFeasTol × M. A binary may sit at 1e-5 in an "integral" solution; with M = 1e7 that admits a phantom slack of 100 ("trickle flow"). Keep M ≤ ~1e6·tolerance headroom, or use indicators.
Big-M caps the LP bound. The LP relaxation can pay a fraction x = b/(b+M·…) to switch constraints off cheaply; the bigger the M, the weaker the root bound.

Reusable Linearization Toolkit

The generic procedure, then implementation-grade gurobipy helpers for each catalog family.

LINEARIZE(model):
  1. Inventory nonlinear terms: products, |.|, min/max, PWL, implications, ratios.
  2. For each term:
     a. Establish finite bounds [L, U] on every continuous variable involved
        (bound propagation; auxiliary bounding LPs when propagation is loose).
     b. Classify the use direction: convex/epigraph use (no binaries needed)
        versus nonconvex use (exact encoding with binaries or SOS).
     c. Pick the reformulation from the catalog; prefer hull-sharp encodings.
     d. Add auxiliary variables and named constraints; record every M together
        with the bound derivation that produced it.
  3. Re-run bound tightening; shrink each recorded M whose bounds improved.
  4. Validate: re-evaluate the original nonlinear expressions at the MILP
     optimum; any mismatch beyond tolerance means a wrong bound or missed case.

Products and the big-M helper

import gurobipy as gp
from gurobipy import GRB


def link_binary_product(model: gp.Model, x: gp.Var, y: gp.Var, name: str) -> gp.Var:
    """Return z enforced to equal x*y for binary x, y (Fortet 1960; Glover & Woolsey 1974)."""
    z = model.addVar(lb=0.0, ub=1.0, name=f"z[{name}]")
    model.addConstr(z <= x, name=f"prod_le_x[{name}]")
    model.addConstr(z <= y, name=f"prod_le_y[{name}]")
    model.addConstr(z >= x + y - 1, name=f"prod_ge[{name}]")
    return z


def link_binary_continuous(model: gp.Model, x: gp.Var, w: gp.Var,
                           lb: float, ub: float, name: str) -> gp.Var:
    """Return z enforced to equal x*w for binary x and continuous w in [lb, ub]."""
    z = model.addVar(lb=min(lb, 0.0), ub=max(ub, 0.0), name=f"z[{name}]")
    model.addConstr(z <= ub * x, name=f"bc_ub_x[{name}]")
    model.addConstr(z >= lb * x, name=f"bc_lb_x[{name}]")
    model.addConstr(z <= w - lb * (1 - x), name=f"bc_w_ub[{name}]")
    model.addConstr(z >= w - ub * (1 - x), name=f"bc_w_lb[{name}]")
    return z


def tight_big_m(coeffs: list[float], lbs: list[float], ubs: list[float], rhs: float) -> float:
    """Smallest M valid for 'sum(c_i w_i) <= rhs + M(1-x)' by interval arithmetic on w."""
    worst = sum(c * (u if c > 0 else l) for c, l, u in zip(coeffs, lbs, ubs))
    return max(worst - rhs, 0.0)


# Tiny synthetic check: maximize x*y + x*w, with w <= 3.5.
m = gp.Model("products")
m.Params.OutputFlag = 0
x = m.addVar(vtype=GRB.BINARY, name="x")
y = m.addVar(vtype=GRB.BINARY, name="y")
w = m.addVar(lb=0.0, ub=5.0, name="w")
m.addConstr(w <= 3.5, name="w_cap")
obj = link_binary_product(m, x, y, "xy") + link_binary_continuous(m, x, w, 0.0, 5.0, "xw")
m.setObjective(obj, GRB.MAXIMIZE)
m.optimize()
assert m.Status == GRB.OPTIMAL
print(f"obj={m.ObjVal:.1f} x={int(round(x.X))} y={int(round(y.X))} w={w.X:.1f}")
print(f"M={tight_big_m([2.0, 3.0], [0.0, 0.0], [5.0, 5.0], 4.0):.0f}")
# Expected: obj=4.5 (x=1, y=1, w=3.5); M=21 (= 2*5 + 3*5 - 4)

Absolute value and max in the nonconvex direction

Both demos below would be answered wrongly by the epigraph-only version: maximizing an epigraph variable lets it float to its upper bound, and t >= w_i alone cannot make a max(...) >= 3 constraint force any single variable up to 3.

import gurobipy as gp
from gurobipy import GRB


def add_abs(model: gp.Model, w: gp.Var, lb: float, ub: float, name: str) -> gp.Var:
    """Return t enforced to equal |w| exactly (sign split with one binary)."""
    t = model.addVar(lb=0.0, ub=max(abs(lb), abs(ub)), name=f"abs[{name}]")
    if lb >= 0:
        model.addConstr(t == w, name=f"abs_id[{name}]")
        return t
    if ub <= 0:
        model.addConstr(t == -w, name=f"abs_neg[{name}]")
        return t
    wp = model.addVar(lb=0.0, ub=ub, name=f"wpos[{name}]")
    wn = model.addVar(lb=0.0, ub=-lb, name=f"wneg[{name}]")
    b = model.addVar(vtype=GRB.BINARY, name=f"sign[{name}]")  # b = 1  <=>  w >= 0
    model.addConstr(w == wp - wn, name=f"abs_split[{name}]")
    model.addConstr(t == wp + wn, name=f"abs_sum[{name}]")
    model.addConstr(wp <= ub * b, name=f"abs_cmpl_p[{name}]")
    model.addConstr(wn <= -lb * (1 - b), name=f"abs_cmpl_n[{name}]")
    return t


def add_max(model: gp.Model, ws: list[gp.Var], lbs: list[float],
            ubs: list[float], name: str) -> gp.Var:
    """Return t enforced to equal max(ws) exactly (one selector binary per term)."""
    t = model.addVar(lb=max(lbs), ub=max(ubs), name=f"max[{name}]")
    pick = model.addVars(len(ws), vtype=GRB.BINARY, name=f"argmax[{name}]")
    top = max(ubs)
    for i, (w, lb_i) in enumerate(zip(ws, lbs)):
        model.addConstr(t >= w, name=f"max_ge[{name},{i}]")
        model.addConstr(t <= w + (top - lb_i) * (1 - pick[i]), name=f"max_le[{name},{i}]")
    model.addConstr(pick.sum() == 1, name=f"max_pick[{name}]")
    return t


# Check 1: maximize |w| over w in [-1, 2]. Epigraph alone would report t at its bound.
m1 = gp.Model("abs_nonconvex_side")
m1.Params.OutputFlag = 0
w = m1.addVar(lb=-1.0, ub=2.0, name="w")
t = add_abs(m1, w, -1.0, 2.0, "w")
m1.setObjective(t, GRB.MAXIMIZE)
m1.optimize()
assert m1.Status == GRB.OPTIMAL
print(f"max |w| = {m1.ObjVal:.1f} at w = {w.X:.1f}")

# Check 2: minimize w1 + w2 subject to max(w1, w2) >= 3 (nonconvex side of max).
m2 = gp.Model("max_lower_bounded")
m2.Params.OutputFlag = 0
w1 = m2.addVar(lb=0.0, ub=4.0, name="w1")
w2 = m2.addVar(lb=0.0, ub=4.0, name="w2")
tmax = add_max(m2, [w1, w2], [0.0, 0.0], [4.0, 4.0], "w12")
m2.addConstr(tmax >= 3.0, name="peak_req")
m2.setObjective(w1 + w2, GRB.MINIMIZE)
m2.optimize()
assert m2.Status == GRB.OPTIMAL
print(f"min w1+w2 = {m2.ObjVal:.1f}")
# Expected: max |w| = 2.0 at w = 2.0; min w1+w2 = 3.0 (one variable pushed to 3)

Implications: big-M and indicator

import gurobipy as gp
from gurobipy import GRB


def add_implication_big_m(model: gp.Model, x: gp.Var, lhs: gp.LinExpr,
                          lhs_ub: float, rhs: float, name: str) -> float:
    """Enforce x = 1 -> lhs <= rhs with M = lhs_ub - rhs. Returns the M used.

    lhs_ub must be a valid upper bound on lhs over the feasible region
    (compute it by interval arithmetic or an auxiliary bounding LP).
    """
    m_val = max(lhs_ub - rhs, 0.0)
    model.addConstr(lhs <= rhs + m_val * (1 - x), name=f"impl[{name}]")
    return m_val


def add_iff_threshold(model: gp.Model, x: gp.Var, w: gp.Var, lb: float, ub: float,
                      thresh: float, eps: float, name: str) -> None:
    """Enforce x = 1 <=> w >= thresh (with tolerance eps on the strict side)."""
    model.addConstr(w >= thresh - (thresh - lb) * (1 - x), name=f"iff_fwd[{name}]")
    model.addConstr(w <= (thresh - eps) + (ub - thresh + eps) * x, name=f"iff_bwd[{name}]")


# Check: x = 1 -> 2 w1 + 3 w2 <= 4, with a bonus of 5 for setting x = 1.
m = gp.Model("implication")
m.Params.OutputFlag = 0
x = m.addVar(vtype=GRB.BINARY, name="x")
w1 = m.addVar(lb=0.0, ub=5.0, name="w1")
w2 = m.addVar(lb=0.0, ub=5.0, name="w2")
used_m = add_implication_big_m(m, x, 2.0 * w1 + 3.0 * w2, lhs_ub=25.0, rhs=4.0, name="cap")
m.setObjective(w1 + w2 + 5.0 * x, GRB.MAXIMIZE)
m.optimize()
assert m.Status == GRB.OPTIMAL
print(f"big-M: obj={m.ObjVal:.1f} x={int(round(x.X))} M={used_m:.0f}")

# Same implication via Gurobi's indicator constraint (no M at all).
m2 = gp.Model("indicator")
m2.Params.OutputFlag = 0
x2 = m2.addVar(vtype=GRB.BINARY, name="x")
v1 = m2.addVar(lb=0.0, ub=5.0, name="v1")
v2 = m2.addVar(lb=0.0, ub=5.0, name="v2")
m2.addGenConstrIndicator(x2, True, 2.0 * v1 + 3.0 * v2 <= 4.0, name="ind_cap")
m2.setObjective(v1 + v2 + 5.0 * x2, GRB.MAXIMIZE)
m2.optimize()
assert m2.Status == GRB.OPTIMAL
print(f"indicator: obj={m2.ObjVal:.1f}")

# Check the iff: with w capped at 2.5 < 3, x is forced to 0.
m3 = gp.Model("iff")
m3.Params.OutputFlag = 0
x3 = m3.addVar(vtype=GRB.BINARY, name="x")
w3 = m3.addVar(lb=0.0, ub=10.0, name="w")
add_iff_threshold(m3, x3, w3, lb=0.0, ub=10.0, thresh=3.0, eps=1e-4, name="th")
m3.addConstr(w3 <= 2.5, name="w_cap")
m3.setObjective(x3, GRB.MAXIMIZE)
m3.optimize()
assert m3.Status == GRB.OPTIMAL
print(f"iff: x={int(round(x3.X))}")
# Expected: big-M obj=10.0 with x=0 and M=21; indicator obj=10.0; iff x=0

Piecewise-linear functions: lambda/SOS2 and incremental

import gurobipy as gp
from gurobipy import GRB


def add_pwl_lambda(model: gp.Model, x: gp.Var, xs: list[float], ys: list[float],
                   name: str) -> gp.Var:
    """Return y = f(x) for the PWL function through (xs[k], ys[k]): lambda method + SOS2."""
    n = len(xs)
    lam = model.addVars(n, lb=0.0, ub=1.0, name=f"lam[{name}]")
    y = model.addVar(lb=min(ys), ub=max(ys), name=f"pwl[{name}]")
    model.addConstr(lam.sum() == 1.0, name=f"pwl_conv[{name}]")
    model.addConstr(x == gp.quicksum(xs[k] * lam[k] for k in range(n)), name=f"pwl_x[{name}]")
    model.addConstr(y == gp.quicksum(ys[k] * lam[k] for k in range(n)), name=f"pwl_y[{name}]")
    model.addSOS(GRB.SOS_TYPE2, [lam[k] for k in range(n)], list(range(1, n + 1)))
    return y


def add_pwl_delta(model: gp.Model, x: gp.Var, xs: list[float], ys: list[float],
                  name: str) -> gp.Var:
    """Return y = f(x) via the incremental (delta) method with filling-order binaries."""
    nseg = len(xs) - 1
    widths = [xs[k + 1] - xs[k] for k in range(nseg)]
    slopes = [(ys[k + 1] - ys[k]) / widths[k] for k in range(nseg)]
    delta = model.addVars(nseg, lb=0.0, ub=widths, name=f"dlt[{name}]")
    fill = model.addVars(max(nseg - 1, 0), vtype=GRB.BINARY, name=f"fill[{name}]")
    for k in range(nseg - 1):
        model.addConstr(delta[k] >= widths[k] * fill[k], name=f"pwl_full[{name},{k}]")
        model.addConstr(delta[k + 1] <= widths[k + 1] * fill[k], name=f"pwl_open[{name},{k}]")
    y = model.addVar(lb=min(ys), ub=max(ys), name=f"pwl[{name}]")
    model.addConstr(x == xs[0] + delta.sum(), name=f"pwl_x[{name}]")
    model.addConstr(y == ys[0] + gp.quicksum(slopes[k] * delta[k] for k in range(nseg)),
                    name=f"pwl_y[{name}]")
    return y


# Check: concave cost (slopes 4, 2, 1); minimizing it is the nonconvex direction.
xs, ys = [0.0, 2.0, 5.0, 10.0], [0.0, 8.0, 14.0, 19.0]
for method in (add_pwl_lambda, add_pwl_delta):
    m = gp.Model(method.__name__)
    m.Params.OutputFlag = 0
    x = m.addVar(lb=0.0, ub=10.0, name="x")
    y = method(m, x, xs, ys, "cost")
    m.addConstr(x >= 7.0, name="demand")
    m.setObjective(y, GRB.MINIMIZE)
    m.optimize()
    assert m.Status == GRB.OPTIMAL
    print(f"{method.__name__}: f(7) = {m.ObjVal:.1f}")
# Expected: both methods print f(7) = 16.0 (= 14 + 1*2 on the third segment)

For pure approximation jobs (a given nonlinear curve, you choose the grid), model.addGenConstrPWL(x, y, xs, ys) lets Gurobi pick its own internal encoding.

Fractional objectives: Charnes–Cooper and Dinkelbach

import numpy as np
import gurobipy as gp
from gurobipy import GRB


def solve_lp_fractional_charnes_cooper(
        c: np.ndarray, alpha: float, d: np.ndarray, beta: float,
        a_mat: np.ndarray, b: np.ndarray) -> tuple[np.ndarray, float]:
    """Maximize (c@x + alpha)/(d@x + beta) s.t. a_mat@x <= b, x >= 0, d@x + beta > 0.

    Charnes & Cooper (1962): substitute t = 1/(d@x + beta), y = t*x. Returns (x*, ratio*).
    """
    n = c.size
    m = gp.Model("charnes_cooper")
    m.Params.OutputFlag = 0
    y = m.addMVar(n, lb=0.0, name="y")
    t = m.addMVar(1, lb=1e-9, name="t")
    m.addConstr(a_mat @ y - b * t <= 0.0, name="scaled_rows")
    m.addConstr(d @ y + beta * t == 1.0, name="normalize")
    m.setObjective(c @ y + alpha * t, GRB.MAXIMIZE)
    m.optimize()
    assert m.Status == GRB.OPTIMAL
    return y.X / t.X[0], m.ObjVal


def solve_binary_fractional_dinkelbach(
        p: np.ndarray, p0: float, q: np.ndarray, q0: float,
        a_mat: np.ndarray, b: np.ndarray, tol: float = 1e-9) -> tuple[np.ndarray, float]:
    """Maximize (p@x + p0)/(q@x + q0) over binary x with a_mat@x <= b, q@x + q0 > 0.

    Dinkelbach (1967): solve max p@x + p0 - lam*(q@x + q0), update lam, repeat until ~0.
    """
    n = p.size
    lam = 0.0
    for _ in range(100):
        m = gp.Model("dinkelbach")
        m.Params.OutputFlag = 0
        x = m.addMVar(n, vtype=GRB.BINARY, name="x")
        m.addConstr(a_mat @ x <= b, name="caps")
        m.setObjective((p - lam * q) @ x + (p0 - lam * q0), GRB.MAXIMIZE)
        m.optimize()
        assert m.Status == GRB.OPTIMAL
        if m.ObjVal <= tol:
            return x.X.round().astype(int), lam
        xv = x.X
        lam = float(p @ xv + p0) / float(q @ xv + q0)
    raise RuntimeError("Dinkelbach did not converge in 100 iterations")


# Check 1: max (3x1 + x2 + 1)/(x1 + 2x2 + 2), x1 + x2 <= 4, x >= 0.
x_cc, ratio_cc = solve_lp_fractional_charnes_cooper(
    np.array([3.0, 1.0]), 1.0, np.array([1.0, 2.0]), 2.0,
    np.array([[1.0, 1.0]]), np.array([4.0]))
print(f"CC: ratio={ratio_cc:.4f} at x={x_cc.round(4)}")

# Check 2: max (4x1+3x2+2x3+1)/(2x1+x2+3x3+1) with x1+x2+x3 <= 2, x binary.
x_dk, ratio_dk = solve_binary_fractional_dinkelbach(
    np.array([4.0, 3.0, 2.0]), 1.0, np.array([2.0, 1.0, 3.0]), 1.0,
    np.array([[1.0, 1.0, 1.0]]), np.array([2.0]))
print(f"Dinkelbach: ratio={ratio_dk:.4f} at x={x_dk}")
# Expected: CC ratio = 2.1667 at x = [4, 0]; Dinkelbach ratio = 2.0 (e.g. x = [0, 1, 0])

Worked Example 1: QAP via the Kaufman–Broeckx Linearization

The Koopmans–Beckmann quadratic assignment problem places n facilities at n locations. With flow matrix F = (f_ik) and distance matrix D = (d_jl), x_ij = 1 if facility i sits at location j:

$$\min \sum_{i,k}\sum_{j,l} f_{ik}, d_{jl}, x_{ij}, x_{kl} \quad \text{s.t.}\quad \sum_j x_{ij} = 1 ;\forall i,\quad \sum_i x_{ij} = 1 ;\forall j,\quad x \in {0,1}^{n\times n}.$$

The objective is a sum of n⁴ binary products. Full Fortet/Glover linearization of every product needs O(n⁴) auxiliary variables. Kaufman & Broeckx (1978) instead apply the binary-times-continuous envelope at the level of whole expressions. Define the interaction cost of placing i at j given the rest of the assignment:

$$c_{ij}(x) = \sum_{k,l} f_{ik}, d_{jl}, x_{kl}, \qquad \text{objective} = \sum_{i,j} x_{ij}, c_{ij}(x).$$

Introduce w_ij ≥ 0 to stand for x_ij·c_ij(x). Since minimization presses w down, only the lower envelope is needed: w_ij ≥ c_ij(x) − a_ij(1 − x_ij), with a_ij an upper bound on c_ij(x). Rearranged into the standard Kaufman–Broeckx constraint:

$$a_{ij}, x_{ij} + \sum_{k,l} f_{ik}, d_{jl}, x_{kl} - w_{ij} \le a_{ij} \qquad \forall i, j.$$

The constant a_ij is a big-M; tighten it with assignment structure. Because Σ_l x_kl = 1 and d ≥ 0, every facility k contributes at most f_ik · max_l d_jl, so a_ij = (Σ_k f_ik)(max_l d_jl) is valid and smaller than the naive Σ_kl f_ik d_jl.

Size: only n² extra continuous variables and n² extra constraints — the most compact known exact QAP linearization. Price: its LP relaxation is extremely weak. On the demo instance below the root LP bound is 0.0 even with the tightened a_ij (the relaxation pays fractional x to switch every KB constraint off). The Adams–Johnson (1994) level-1 RLT linearization gives far stronger bounds at O(n⁴) size — see Advanced Techniques and the dedicated quadratic-assignment-problem skill for that trade-off.

import itertools
import numpy as np
import gurobipy as gp
from gurobipy import GRB


def qap_instance(n: int, seed: int) -> tuple[np.ndarray, np.ndarray]:
    """Random symmetric QAP instance: integer flows/distances in {0,...,9}, zero diagonal."""
    rng = np.random.default_rng(seed)
    f = rng.integers(0, 10, size=(n, n))
    d = rng.integers(0, 10, size=(n, n))
    f, d = (f + f.T) // 2, (d + d.T) // 2
    np.fill_diagonal(f, 0)
    np.fill_diagonal(d, 0)
    return f.astype(float), d.astype(float)


def qap_objective(perm: np.ndarray, f: np.ndarray, d: np.ndarray) -> float:
    """Quadratic objective for an assignment: facility i sits at location perm[i]."""
    return float((f * d[np.ix_(perm, perm)]).sum())


def solve_qap_kaufman_broeckx(f: np.ndarray, d: np.ndarray,
                              time_limit: float = 60.0) -> tuple[np.ndarray, float]:
    """Solve the QAP via the Kaufman-Broeckx linearization. Returns (perm, objective)."""
    n = f.shape[0]
    cost = np.einsum("ik,jl->ijkl", f, d)        # cost[i,j,k,l] = f[i,k] * d[j,l]
    a = np.outer(f.sum(axis=1), d.max(axis=1))   # tightened a_ij (needs f, d >= 0)
    m = gp.Model("qap_kb")
    m.Params.OutputFlag = 0
    m.Params.TimeLimit = time_limit
    x = m.addVars(n, n, vtype=GRB.BINARY, name="x")
    w = m.addVars(n, n, lb=0.0, name="w")
    m.addConstrs((x.sum(i, "*") == 1 for i in range(n)), name="facility")
    m.addConstrs((x.sum("*", j) == 1 for j in range(n)), name="location")
    m.addConstrs(
        (a[i, j] * x[i, j]
         + gp.quicksum(cost[i, j, k, l] * x[k, l] for k in range(n) for l in range(n))
         - w[i, j] <= a[i, j]
         for i in range(n) for j in range(n)),
        name="kb")
    m.setObjective(w.sum(), GRB.MINIMIZE)
    m.optimize()
    assert m.Status == GRB.OPTIMAL or (m.Status == GRB.TIME_LIMIT and m.SolCount > 0)
    perm = np.array([next(j for j in range(n) if x[i, j].X > 0.5) for i in range(n)])
    return perm, m.ObjVal


f, d = qap_instance(n=5, seed=3)
perm, obj = solve_qap_kaufman_broeckx(f, d)
assert abs(obj - qap_objective(perm, f, d)) < 1e-6   # independent validation
brute = min(qap_objective(np.array(p), f, d) for p in itertools.permutations(range(5)))
print(f"KB optimum = {obj:.0f}, brute force = {brute:.0f}, perm = {perm}")
# Expected: KB optimum = 204, brute force = 204, perm = [3 1 4 0 2]

Worked Example 2: Fixed-Charge Network with Piecewise-Linear Costs

Single-commodity min-cost flow on a digraph (V, A) where each arc a charges a fixed cost K_a when opened plus a concave piecewise-linear flow cost g_a(w_a) (decreasing marginal rates — economies of scale):

$$\min \sum_{a \in A}\Big(K_a, y_a + g_a(w_a)\Big) \quad \text{s.t.}\quad \sum_{a \in \delta^+(v)} w_a - \sum_{a \in \delta^-(v)} w_a = b_v ;\forall v, \qquad 0 \le w_a \le u_a, y_a,\qquad y_a \in {0,1}.$$

Two linearizations interact here:

Fixed charge. w_a ≤ M_a y_a is a binary-times-continuous envelope in disguise. The tight M_a is min(u_a, total demand): no arc ever carries more than the network demands, even if its nominal capacity is larger. This single tightening is the classic fix for weak fixed-charge network LPs.
Concave PWL cost. Minimizing a concave g_a is the nonconvex direction — the LP lower envelope is the chord under the curve, so binaries (or SOS2) are mandatory. The incremental method fits naturally: its first segment variable can couple directly to the fixed-charge binary (δ₁ ≤ width₁·y_a), so the arc-opening binary and the PWL chain share one variable instead of two. Croxton, Gendron & Magnanti (2003) study exactly this model class and show the standard PWL encodings have the same LP bound.

import numpy as np
import gurobipy as gp
from gurobipy import GRB


def fcn_instance(seed: int) -> dict:
    """Small fixed-charge network: node 0 supplies 20 units; nodes 3, 4 demand 12 and 8."""
    rng = np.random.default_rng(seed)
    arcs = [(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
    balance = {0: 20.0, 1: 0.0, 2: 0.0, 3: -12.0, 4: -8.0}
    arc = {}
    for a in arcs:
        s1 = float(rng.integers(4, 9))
        arc[a] = {"cap": float(rng.integers(8, 21)),
                  "fixed": float(rng.integers(20, 61)),
                  "slopes": [s1, 0.6 * s1, 0.3 * s1]}   # concave: economies of scale
    return {"arcs": arcs, "balance": balance, "arc": arc}


def pwl_points(cap: float, slopes: list[float]) -> tuple[list[float], list[float]]:
    """Breakpoints (xs, ys) of the concave arc cost on [0, cap], equal-width segments."""
    nseg = len(slopes)
    xs = [cap * k / nseg for k in range(nseg + 1)]
    ys = [0.0]
    for k in range(nseg):
        ys.append(ys[-1] + slopes[k] * (xs[k + 1] - xs[k]))
    return xs, ys


def pwl_eval(v: float, xs: list[float], ys: list[float]) -> float:
    """Evaluate the PWL function defined by (xs, ys) at v."""
    for k in range(len(xs) - 1):
        if v <= xs[k + 1] + 1e-9:
            frac = (v - xs[k]) / (xs[k + 1] - xs[k])
            return ys[k] + frac * (ys[k + 1] - ys[k])
    return ys[-1]


def solve_fixed_charge_pwl(inst: dict) -> tuple[dict, float]:
    """Min-cost flow with fixed charges and concave PWL flow costs. Returns (flows, cost)."""
    total_demand = sum(-b for b in inst["balance"].values() if b < 0)
    m = gp.Model("fcnf_pwl")
    m.Params.OutputFlag = 0
    flow, use, cost_terms = {}, {}, []
    for a in inst["arcs"]:
        d = inst["arc"][a]
        cap = min(d["cap"], total_demand)            # tightened arc big-M
        xs, ys = pwl_points(cap, d["slopes"])
        nseg = len(d["slopes"])
        widths = [xs[k + 1] - xs[k] for k in range(nseg)]
        use[a] = m.addVar(vtype=GRB.BINARY, name=f"use[{a}]")
        delta = m.addVars(nseg, lb=0.0, ub=widths, name=f"dlt[{a}]")
        fill = m.addVars(nseg - 1, vtype=GRB.BINARY, name=f"fill[{a}]")
        m.addConstr(delta[0] <= widths[0] * use[a], name=f"open[{a}]")
        for k in range(nseg - 1):
            m.addConstr(delta[k] >= widths[k] * fill[k], name=f"full[{a},{k}]")
            m.addConstr(delta[k + 1] <= widths[k + 1] * fill[k], name=f"next[{a},{k}]")
        flow[a] = m.addVar(lb=0.0, ub=cap, name=f"w[{a}]")
        m.addConstr(flow[a] == delta.sum(), name=f"link[{a}]")
        cost_terms.append(d["fixed"] * use[a]
                          + gp.quicksum(d["slopes"][k] * delta[k] for k in range(nseg)))
    for v, b in inst["balance"].items():
        m.addConstr(
            gp.quicksum(flow[a] for a in inst["arcs"] if a[0] == v)
            - gp.quicksum(flow[a] for a in inst["arcs"] if a[1] == v) == b,
            name=f"balance[{v}]")
    m.setObjective(gp.quicksum(cost_terms), GRB.MINIMIZE)
    m.optimize()
    assert m.Status == GRB.OPTIMAL
    return {a: flow[a].X for a in inst["arcs"]}, m.ObjVal


def validate(inst: dict, flows: dict, reported: float) -> None:
    """Recompute cost and conservation independently of the model."""
    total_demand = sum(-b for b in inst["balance"].values() if b < 0)
    cost = 0.0
    for a, v in flows.items():
        d = inst["arc"][a]
        cap = min(d["cap"], total_demand)
        if v > 1e-6:
            xs, ys = pwl_points(cap, d["slopes"])
            cost += d["fixed"] + pwl_eval(v, xs, ys)
    for node, b in inst["balance"].items():
        out_v = sum(v for a, v in flows.items() if a[0] == node)
        in_v = sum(v for a, v in flows.items() if a[1] == node)
        assert abs(out_v - in_v - b) < 1e-6, f"conservation violated at {node}"
    assert abs(cost - reported) < 1e-6, f"cost mismatch: {cost} vs {reported}"


inst = fcn_instance(seed=7)
flows, obj = solve_fixed_charge_pwl(inst)
validate(inst, flows, obj)
used = {a: round(v, 2) for a, v in flows.items() if v > 1e-6}
print(f"optimal cost = {obj:.2f}, flows = {used}")
# Expected: optimal cost = 456.77 with 5 arcs used, e.g. flows
# {(0,1): 11.0, (0,2): 9.0, (1,3): 11.0, (2,3): 1.0, (2,4): 8.0}

Advanced Techniques

Reformulation-Linearization Technique (RLT)

Sherali & Adams (1990) generate stronger linearizations systematically: multiply each constraint by each binary variable and by its complement, expand, then replace every product with a linearization variable. Level-1 RLT applied to the QAP (Adams & Johnson 1994) yields the variables y_ijkl = x_ij x_kl plus consistency rows such as Σ_k y_ijkl = x_ij; its LP bound dominates Kaufman–Broeckx, Lawler, and most Lagrangian bounds, at the price of O(n⁴) variables. The general recipe — multiply, expand, substitute — is the standard way to trade model size for root-bound strength on any 0-1 polynomial program.

Logarithmic piecewise-linear formulations

The lambda and incremental methods use K−1 or K binaries for K segments. Vielma & Nemhauser (2011), modeling disjunctive constraints with a logarithmic number of binary variables, encode the active segment with ⌈log₂K⌉ binaries using a Gray code so adjacent segments differ in one bit. For cost curves with dozens of breakpoints (price ladders, unit-commitment heat curves) this shrinks the binary count dramatically while remaining locally ideal. Use it when K ≳ 16 and the model has many PWL terms; below that the simpler encodings branch as well.

Convex-hull formulations for disjunctions

For a disjunction w ∈ P₁ ∨ w ∈ P₂ with bounded polyhedra, Balas (1998) gives the extended hull formulation: duplicate w into copies w¹, w², enforce each copy inside its (scaled) polyhedron A_i w^i ≤ b_i λ_i, and add w = w¹ + w², λ₁ + λ₂ = 1, λ binary. Its LP relaxation equals the convex hull of the disjunction — strictly tighter than any big-M version — but it doubles the variables per disjunction. Rule of thumb: hull formulations pay off when the disjunction sits in few constraints of a hard model; big-M with tight constants wins when there are thousands of small disjunctions.

Optimization-based bound tightening for big-M

Interval arithmetic ignores constraint interaction, so its Ms are often loose. OBBT solves, for each expression e = a·w that needs a bound, the auxiliary LP max{e : LP relaxation of the model} and uses the optimum as the new bound. Iterating propagation and OBBT shrinks Ms monotonically; each shrink tightens the LP relaxation of every implication that uses it. Budget OBBT like presolve: run it once on the root relaxation, only for the Ms that appear in constraints the LP actually leaves slack.

Piecewise McCormick for continuous bilinear terms

When z = v·w with both variables continuous must be handled inside a MILP, partition the domain of one variable into segments, apply the McCormick envelope per segment, and select the active segment with binaries (or SOS1). The relaxation error of McCormick shrinks quadratically with segment width, so even 4–8 partitions help substantially. Compare against simply setting model.Params.NonConvex = 2 and letting Gurobi spatial-branch: for a handful of bilinear terms the built-in method usually wins; for structured products that repeat thousands of times, a hand-built piecewise McCormick with shared partition binaries can dominate.

Practical Challenges

Trickle flow: solutions violate the logic although the solver says optimal. With IntFeasTol = 1e-5 (Gurobi default) a binary can sit at 1e-5 while counted as 0, so a constraint w ≤ M·y with M = 1e7 silently admits w = 100. Shrink M from bounds, switch the constraint to an indicator, or tighten IntFeasTol — in that order of preference.

The LP bound collapses after linearization. Compact big-M linearizations (Kaufman–Broeckx is the extreme case — root bound 0.0 in the worked example) buy small size with weak bounds. If branch-and-bound stalls with a near-zero bound, move along the size/strength curve: tighter Ms first, then disaggregated or RLT-style variables, then hull formulations.

Linearizing every product blows up memory. A quadratic objective over n² binaries has O(n⁴) products. Before generating them all, check whether an expression-level trick (KB style), a solver-native quadratic mode, or a Lagrangian/decomposition approach avoids the expansion entirely. Generating 10⁸ rows is a modeling failure, not a solver failure.

Epigraph shortcut used in the wrong direction. t ≥ |w| linearized as t ≥ ±w is correct only while something presses t down. The bug appears later, when someone adds a term that rewards large t and the model silently reports t at its upper bound. Audit every abs/min/max linearization whenever the objective or constraint sense changes; encode exactly (with binaries) if the use direction is unclear.

Strict inequalities in reverse implications. "If w < b then x = 1" has no exact MILP form; you must pick ε > 0 and model w ≤ b − ε on one branch. Choose ε well above FeasibilityTol (1e-6), document it, and check the optimum is not sitting inside the ε gap.

Indicator constraints solve slower than expected. Indicators are exact and M-free, but the solver's internal reformulation may be weaker than a hand-tightened big-M when good bounds exist. Benchmark both on a representative instance: keep indicators when bounds are poor or Ms would exceed ~1e6; keep explicit big-M when tight constants are available.

SOS2 versus binary PWL encodings. SOS2 adds no binaries but relies on special branching; with many PWL terms, binary encodings (incremental, multiple choice, logarithmic) usually give better bounds because cuts and presolve act on binaries. If the SOS2 model drifts with a weak bound, re-encode with binaries before blaming the solver.

Numerical conditioning after linearization. Mixing M = 1e6 rows with 1e-3 cost coefficients yields coefficient ranges the solver flags (Warning: Model contains large coefficient range). Rescale units (move from grams to tonnes, cents to thousands of dollars) so all matrix coefficients land within roughly [1e-3, 1e6], and re-derive Ms after rescaling.

Tools & Libraries

Library / feature	When to use	Note
gurobipy general constraints (`addGenConstrAbs/Max/Min/And/Or/Indicator`)	abs, min/max, logic without manual Ms	solver picks the reformulation; bounds still matter
gurobipy `addGenConstrPWL`	piecewise-linear objective/constraint terms	pass breakpoints; no manual SOS2 needed
gurobipy `Params.NonConvex = 2`	bilinear/quadratic terms left as-is	spatial branch-and-bound; baseline to beat before hand-linearizing
Pyomo `Piecewise` + GDP	solver-agnostic models; disjunctive modeling	GDP transformations: `gdp.bigm` and `gdp.hull` map directly to this skill
OR-Tools CP-SAT (`AddMultiplicationEquality`, channeling)	integer products and logic at scale	CP propagation replaces linearization for pure-integer terms
PySCIPOpt	open-source MINLP with indicator/SOS support	SCIP applies OBBT and its own reformulations
docplex (CPLEX)	logical constraints, PWL via `model.piecewise`	automatic indicator handling similar to Gurobi

Output Format

A complete linearization deliverable contains:

Reformulation summary table — one row per nonlinear term:

Term	Technique	Aux vars	Aux constrs	Exact / relaxation
x_ij·c_ij(x) (n² terms)	Kaufman–Broeckx envelope	n² cont.	n²	exact
g_a(w_a), concave, 3 segs	incremental + shared fixed-charge binary	3 cont. + 2 bin. per arc	5 per arc	exact

Big-M provenance table — every constant with its derivation, so reviewers can audit:

Constraint family M value Derived from Slack at optimum

kb[i,j] a_ij = (Σ_k f_ik)(max_l d_jl) row sums of F, row maxima of D reported per row
Model statistics before/after — variables (cont./bin.), constraints, nonzeros, and the root LP bound of the linearized model versus the best known bound, so the size/strength trade actually made is visible.
Validation report — the original nonlinear objective re-evaluated at the MILP optimum by independent code (like qap_objective and validate above), the absolute and relative gap to the reported MILP objective, and a pass/fail line per logical implication checked at the solution.
Reproducible script — instance generator seed, solver version, parameter settings (IntFeasTol, TimeLimit, NonConvex), and the exact commands run.

Constraint family	M value	Derived from	Slack at optimum
kb[i,j]	a_ij = (Σ_k f_ik)(max_l d_jl)	row sums of F, row maxima of D	reported per row

State explicitly which reformulations are exact and which are relaxations/approximations, and give the approximation error bound for any PWL grid used.

Questions to Ask

Which variables in each product are binary, integer, or continuous — and what are their tightest known bounds?
Is the nonlinear term used in its convex direction (epigraph is enough) or do we need the exact encoding with binaries?
What solver and version are available — are indicator constraints, SOS2, native PWL, or a nonconvex quadratic mode on the table?
How many instances of each term exist — does the reformulation stay within memory at O(count × aux vars)?
For PWL approximations of smooth functions: what error tolerance is acceptable, and over what domain?
For fractional objectives: is the denominator provably positive over the feasible region?
Are there strict inequalities or "exactly when" conditions that need an explicit ε?
What integrality and feasibility tolerances will the production run use (they bound the largest safe M)?

Related Skills

milp-modeling-gurobi — when the question is broader model construction, variable design, or Gurobi API usage rather than a specific nonlinear-term reformulation.
integer-programming-techniques — when the linearized model solves slowly and needs formulation strengthening, valid inequalities, or branching insight.
quadratic-assignment-problem — when the user works on the QAP itself and needs the full formulation landscape (Lawler, RLT, bounds) beyond the linearization shown here.
facility-location-problem — when fixed-charge and big-M patterns appear in location models, where the same envelope and tightening ideas drive formulation quality.

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