Linear program
Named by 5 essays across one field — each of them below, with the objects they name alongside it.
Two numbers that have to meet
Every linear program has a shadow — a second program built from the same numbers read the other way, whose minimum can never fall below the first's maximum. That much is a one-line calculation; the theorem is that the two numbers are always exactly equal.
What a constraint is worth
The rung below settled that a linear program and its dual reach the same number. This one asks what the dual's variables are, and the answer converts a solution into a rate for every constraint — piecewise constant, zero on the constraints that are not doing any work.
The value from both sides
Two choosers move at the same instant, and each asks the cautious question — how much can be guaranteed, whatever the other does. With pure choices the two answers are usually different numbers; allow a probability and they are forced to be the same one.
A lottery over whole assignments
A table of shares in which every person's shares add to one task and every task is exactly covered is never anything more than a mixture of whole assignments — and finding the mixture is a matter of taking one complete assignment out at a time.
A signal both can see
Two choosers who randomise privately can reach a set of outcomes that is smaller, and worse, than the set they reach when a device draws one cell and whispers each of them their half of it. Nothing is enforced and nobody is bound, and the arrangement is stable anyway.
Named alongside it
The objects these essays reach for when they reach for this one.
ConvexityDualityCorrelated equilibriumExistence proofFeasible regionMinimaxMixed strategyNash equilibriumVertex enumerationZero-sum gameAssignmentBest reply