Winning Condition and Loss Operators #
This module defines the operator-valued winning and loss expressions attached to an LCS game and a projector strategy.
Its main result is a sum-of-squares decomposition of the local loss operator, following the paper's algebraic winning-condition identities.
Local Operators #
This section defines the winning assignments for a constraint and the associated local winning and loss operators.
The assignments satisfying equation i in the game game.
Equations
- MIPRE.LCS.winningAssignments game i = {α : G.Assignment i | ∑ j : ↥(G.V i), α j = game.b i}
Instances For
The local winning operator for a single edge (i, j).
Equations
- MIPRE.LCS.localWinningOperator game strat i j = ∑ x ∈ MIPRE.LCS.winningAssignments game i, strat.E i x * strat.F (↑j) (x j)
Instances For
The local loss operator for a single edge (i, j).
Equations
- MIPRE.LCS.localLossOperator game strat i j = 1 - MIPRE.LCS.localWinningOperator game strat i j
Instances For
Winning Projector Identities #
Two local projector identities used in the sum-of-squares derivation.
Lemma 4.7.1: the sum of winning projectors equals the signed row-product expression.
Local Loss SOS #
This section derives the sum-of-squares decomposition of the local loss operator by a sequence of private rewriting lemmas.
Global Operators #
The overall winning and loss operators are obtained by averaging the local quantities over the question graph of the game.
The total winning operator v is the average of the local winning operators.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The total Loss Operator 1 - v.
Equations
- MIPRE.LCS.lossOperator game strat = 1 - MIPRE.LCS.winningOperator game strat