A linear complementarity problem asks for real vectors satisfying
for a prescribed real matrix and vector . The inequalities hold componentwise. Since every product is nonnegative, the final equality means for every index: each paired component has at least one zero. The linear complementarity problem is a feasibility problem; it need not come with an objective function. Its affine equation does not make the product condition linear.[ref]
For an original two-variable example, take
The pair and satisfies all three conditions. Guessing which member of each pair vanishes reduces the remaining equations to a candidate linear system, but each candidate must still satisfy nonnegativity. Choosing here gives and fails that check. Some data admit no solution: in one dimension, gives for every .
The connection with quadratic programming explains one important tractable case. For a symmetric matrix , consider
The Karush-Kuhn-Tucker conditions set its gradient equal to a nonnegative constraint multiplier and impose complementary slackness, giving precisely the displayed linear complementarity problem. If is a positive-definite matrix, the objective function is strictly convex and grows without bound as , so a unique minimizer exists. This conclusion uses that additional matrix property, not merely the word linear.
There is also a short uniqueness proof. For two complementary pairs,
Positive definiteness then forces . This argument gives a directly checkable reason for uniqueness in the stipulated case.
In unilateral contact complementarity, a nonnegative separation pairs with a nonnegative normal contact force: an open gap has zero contact reaction, while a positive reaction requires closed contact. A time-discretized rigid body model can relate its next-step gap affinely to the unknown reaction, producing a linear complementarity problem. The particular discretization and impact law determine that affine relation; the algebraic form alone does not select the physical contact model.[ref]

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The Linear Complementarity Problem (LCP) is a mathematical problem that involves finding vectors that satisfy certain linear inequalities and equations. Specifically, the LCP can be formally defined as follows: Given a matrix \( M \) and a vector \( q \), the goal is to find a vector \( z \) such that: 1. \( z \geq 0 \) (the vector \( z \) is element-wise non-negative), 2.