Documentation

MIPRE.Background.LIDT.Co.SelfImprovement.Theorems.Results.AddInUStep12.Algebra

Add-in-u Cauchy--Schwarz algebraic alignment #

Operator identities that align the differences of the add-in-u scalar chains with the forms used in the Cauchy--Schwarz estimates of the paper: the counterpart of the vendored SelfImprovement/Theorems/Results/AddInUStep12/Algebra.lean (under MIPRE/Background/LIDT/MIPStarRE/LDT/) in the port of planning/c6b-plan.md (milestone M10, section "Port conventions").

Contents #

Every scalar is strategy.state.ev of a joint operator; leftTensor A, rightTensor A and opTensor A B are strategy.state.L A, strategy.state.R A and strategy.state.opTensor A B. No statement carries a swap, density or normalization hypothesis, and none of the vendored ones did.

The vendored file imports SelfImprovement/Theorems/Thresholds/Final.lean, wholly classical and without a Co file; so does this one.

Not ported #

Every declaration of the vendored file has a counterpart here.

References #

In LionSR/MIPStarRE at commit 507e8122, not in this repository:

Algebraic CS-alignment for the add-in-u Step 1/2 differences #

Pure operator-algebra rewrites that bring the differences addInUCSChainQ1 - addInUCSChainQ0 and addInUCSChainQ2 - addInUCSChainQ1 into the shapes required by the paper's Cauchy--Schwarz steps eq:move-one-cauchy-schwarz and eq:move-another-cauchy-schwarz (self_improvement.tex, lines 261--266 and 285--289). The reverse-difference companions give the downstream orientation Qโ‚€ - Qโ‚ and Qโ‚ - Qโ‚‚.

They do not discharge the Cauchy--Schwarz estimate itself; they reduce the raw |Qโ‚ - Qโ‚€| โ‰ค โˆš(2ฮด) and |Qโ‚ - Qโ‚‚| โ‰ค โˆš(2ฮด) bounds to a sandwich-form Cauchy--Schwarz on the resulting D ยท (M^u_h โŠ— T_h) ยท D' expression, plus the two square-root inputs addInU_pointMeasurement_snd_selfConsistency and addInU_filtered_sandwiched_tensor_sum_le_one.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_step1_pointwise_op_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (S : SymModel ๐”“ K) (M Av Th : ๐”“) :
S.opTensor (Av * M) (Th * Av) - S.opTensor M (Av * Th * Av) = (S.L Av - S.R Av) * (S.opTensor M Th * S.R Av)

Operator identity for the Qโ‚€ โ†’ Qโ‚ move: (A^v M) โŠ— (T A^v) โˆ’ M โŠ— (A^v T A^v) = (A^v โŠ— I โˆ’ I โŠ— A^v) ยท (M โŠ— T) ยท (I โŠ— A^v).

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_step2_pointwise_op_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (S : SymModel ๐”“ K) (M Av Th : ๐”“) :
S.opTensor (Av * M * Av) Th - S.opTensor (Av * M) (Th * Av) = S.L Av * (S.opTensor M Th * (S.L Av - S.R Av))

Operator identity for the Qโ‚ โ†’ Qโ‚‚ move: (A^v M A^v) โŠ— T โˆ’ (A^v M) โŠ— (T A^v) = (A^v โŠ— I) ยท (M โŠ— T) ยท (A^v โŠ— I โˆ’ I โŠ— A^v).

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_step3_pointwise_op_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (S : SymModel ๐”“ K) (Au Av Mh Th : ๐”“) :
S.opTensor (Av * Mh * Av) Th - S.opTensor (Au * Mh * Av) Th = S.opTensor ((Av - Au) * Mh * Av) Th

Operator algebra reduction for the Qโ‚‚ โ†’ Qโ‚ƒ add-in-u step.

The operator difference of the bipartite-tensor expectations of A^v ยท H^u_h ยท A^v and A^u ยท H^u_h ยท A^v (with shared right factor T_h) factors as (A^v โˆ’ A^u) ยท H^u_h ยท A^v on the left tensor factor, leaving the right factor T_h untouched.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_step4_pointwise_op_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (S : SymModel ๐”“ K) (Au Av Mh Th : ๐”“) :
S.opTensor (Au * Mh * Av) Th - S.opTensor (Au * Mh * Au) Th = S.opTensor (Au * Mh * (Av - Au)) Th

Operator algebra reduction for the Qโ‚ƒ โ†’ Qโ‚„ add-in-u step.

The operator difference of the bipartite-tensor expectations of A^u ยท H^u_h ยท A^v and A^u ยท H^u_h ยท A^u (with shared right factor T_h) factors as A^u ยท H^u_h ยท (A^v โˆ’ A^u) on the left tensor factor, leaving the right factor T_h untouched.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_cs_chain_step1_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) :
addInUCSChainQ1 params strategy T - addInUCSChainQ0 params strategy T = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ h : MIPStarRE.LDT.Polynomial params, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.2; have Mh := (sandwichedPolynomialSubMeasAt params strategy T uv.1).outcome h; strategy.state.ev ((strategy.state.L Av - strategy.state.R Av) * (strategy.state.opTensor Mh (T.outcome h) * strategy.state.R Av))

Algebraic CS-alignment for the Qโ‚€ โ†’ Qโ‚ step.

Rewrites the difference addInUCSChainQ1 - addInUCSChainQ0 in the exact form appearing on the LHS of eq:move-one-cauchy-schwarz (paper lines 261--266): the inner-product of the commutator A^v_{h(v)} โŠ— I โˆ’ I โŠ— A^v_{h(v)} with M^u_h โŠ— T_h ยท (I โŠ— A^v_{h(v)}), averaged over (u, v) and summed over h.

This identity is purely algebraic; the actual โˆš(2ฮด) bound still requires the operator Cauchy--Schwarz step plus addInU_pointMeasurement_snd_selfConsistency.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_cs_chain_step2_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) :
addInUCSChainQ2 params strategy T - addInUCSChainQ1 params strategy T = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ h : MIPStarRE.LDT.Polynomial params, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.2; have Mh := (sandwichedPolynomialSubMeasAt params strategy T uv.1).outcome h; strategy.state.ev (strategy.state.L Av * (strategy.state.opTensor Mh (T.outcome h) * (strategy.state.L Av - strategy.state.R Av)))

Algebraic CS-alignment for the Qโ‚ โ†’ Qโ‚‚ step.

Rewrites the difference addInUCSChainQ2 - addInUCSChainQ1 in the exact form appearing on the LHS of eq:move-another-cauchy-schwarz (paper lines 285--289): the inner-product of (A^v_{h(v)} ยท M^u_h) โŠ— T_h with the commutator A^v_{h(v)} โŠ— I โˆ’ I โŠ— A^v_{h(v)}, averaged over (u, v) and summed over h. The Lean statement keeps the equivalent factored form (A^v_{h(v)} โŠ— I) ยท (M^u_h โŠ— T_h) before the commutator.

This identity is purely algebraic; the actual โˆš(2ฮด) bound still requires the operator Cauchy--Schwarz step plus addInU_pointMeasurement_snd_selfConsistency and addInU_filtered_sandwiched_tensor_sum_le_one.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_cs_chain_step1_reverse_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) :
addInUCSChainQ0 params strategy T - addInUCSChainQ1 params strategy T = -MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ h : MIPStarRE.LDT.Polynomial params, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.2; have Mh := (sandwichedPolynomialSubMeasAt params strategy T uv.1).outcome h; strategy.state.ev ((strategy.state.L Av - strategy.state.R Av) * (strategy.state.opTensor Mh (T.outcome h) * strategy.state.R Av))

Reverse-orientation form of addInU_cs_chain_step1_diff_eq.

This is the same algebraic identity as the Qโ‚€ โ†’ Qโ‚ rewrite, stated in the Qโ‚€ - Qโ‚ orientation used by the later absolute-value chain.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_cs_chain_step2_reverse_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) :
addInUCSChainQ1 params strategy T - addInUCSChainQ2 params strategy T = -MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ h : MIPStarRE.LDT.Polynomial params, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.2; have Mh := (sandwichedPolynomialSubMeasAt params strategy T uv.1).outcome h; strategy.state.ev (strategy.state.L Av * (strategy.state.opTensor Mh (T.outcome h) * (strategy.state.L Av - strategy.state.R Av)))

Reverse-orientation form of addInU_cs_chain_step2_diff_eq.

This is the same algebraic identity as the Qโ‚ โ†’ Qโ‚‚ rewrite, stated in the Qโ‚ - Qโ‚‚ orientation used by the later absolute-value chain.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_cs_chain_step3_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) :
addInUCSChainQ2 params strategy T - addInUCSChainQ3 params strategy T = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ h : MIPStarRE.LDT.Polynomial params, have Au := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.1; have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.2; have Mh := (sandwichedPolynomialSubMeasAt params strategy T uv.1).outcome h; strategy.state.ev (strategy.state.opTensor ((Av - Au) * Mh * Av) (T.outcome h))

Algebraic CS-alignment for the Qโ‚‚ โ†’ Qโ‚ƒ step.

Rewrites the difference addInUCSChainQ2 - addInUCSChainQ3 in the exact form appearing on the LHS of eq:change-one-cauchy-schwarz (paper lines 306--311): the expectation of ((A^v_{h(v)} - A^u_{h(u)}) ยท H^u_h ยท A^v_{h(v)}) โŠ— T_h, averaged over (u, v) and summed over h.

The paper writes the middle factor as the fiber operator M^u_o; in this formalization the preceding o-sum has already been collapsed along o = h(u), so the same factor appears as H^u_h = (sandwichedPolynomialSubMeasAt params strategy T u).outcome h.

This identity is purely algebraic; the operator Cauchy--Schwarz estimate is proved downstream by add_in_u_cs_chain_q2_q3_factored_cs.

Selection-parametrized Step 1/2 algebraic identities #

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step1_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ1 params strategy M T S - addInUSelectedCSChainQ0 params strategy M T S = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev ((strategy.state.L Av - strategy.state.R Av) * (strategy.state.opTensor Moh (T.outcome ah.2) * strategy.state.R Av))

Algebraic CS-alignment for the selected Qโ‚€ โ†’ Qโ‚ step.

This is the selection-parametrized form of addInU_cs_chain_step1_diff_eq. The identity is stated in the same orientation as the diagonal chain, namely as Qโ‚ - Qโ‚€. It rewrites the scalar difference using the same commutator A^v_{h(v)} โŠ— I โˆ’ I โŠ— A^v_{h(v)}, but sums only over the selected pairs (o,h) โˆˆ S_u and leaves the arbitrary outcome operator M^u_o in place.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step2_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ2 params strategy M T S - addInUSelectedCSChainQ1 params strategy M T S = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev (strategy.state.L Av * (strategy.state.opTensor Moh (T.outcome ah.2) * (strategy.state.L Av - strategy.state.R Av)))

Algebraic CS-alignment for the selected Qโ‚ โ†’ Qโ‚‚ step.

This is the selection-parametrized form of addInU_cs_chain_step2_diff_eq, with the arbitrary selected outcome operator M^u_o in the left tensor factor.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step1_reverse_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ0 params strategy M T S - addInUSelectedCSChainQ1 params strategy M T S = -MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev ((strategy.state.L Av - strategy.state.R Av) * (strategy.state.opTensor Moh (T.outcome ah.2) * strategy.state.R Av))

Reverse-orientation selected form of addInU_selected_cs_chain_step1_diff_eq.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step2_reverse_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ1 params strategy M T S - addInUSelectedCSChainQ2 params strategy M T S = -MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev (strategy.state.L Av * (strategy.state.opTensor Moh (T.outcome ah.2) * (strategy.state.L Av - strategy.state.R Av)))

Reverse-orientation selected form of addInU_selected_cs_chain_step2_diff_eq.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step3_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ2 params strategy M T S - addInUSelectedCSChainQ3 params strategy M T S = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Au := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.1; have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev (strategy.state.opTensor ((Av - Au) * Moh * Av) (T.outcome ah.2))

Algebraic CS-alignment for the selected Qโ‚‚ โ†’ Qโ‚ƒ step.

This rewrites the first point-replacement move for an arbitrary selected outcome family. The only changed factor is the left copy of the point projector, from A^v_{h(v)} to A^u_{h(u)}.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step4_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ3 params strategy M T S - addInUSelectedCSChainQ4 params strategy M T S = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Au := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.1; have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev (strategy.state.opTensor (Au * Moh * (Av - Au)) (T.outcome ah.2))

Algebraic CS-alignment for the selected Qโ‚ƒ โ†’ Qโ‚„ step.

This rewrites the second point-replacement move for an arbitrary selected outcome family. The only changed factor is the right copy of the point projector, from A^v_{h(v)} to A^u_{h(u)}.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step3_reverse_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ3 params strategy M T S - addInUSelectedCSChainQ2 params strategy M T S = -MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Au := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.1; have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev (strategy.state.opTensor ((Av - Au) * Moh * Av) (T.outcome ah.2))

Reverse-orientation selected form of addInU_selected_cs_chain_step3_diff_eq.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_selected_cs_chain_step4_reverse_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] {Outcome : Type u_3} [Fintype Outcome] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (M : IdxSubMeas (MIPStarRE.LDT.Point params) Outcome ๐”“) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) (S : MIPStarRE.LDT.SelfImprovement.AddInUSelection params Outcome) :
addInUSelectedCSChainQ4 params strategy M T S - addInUSelectedCSChainQ3 params strategy M T S = -MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ ah โˆˆ MIPStarRE.LDT.SelfImprovement.addInUSelectionPairs params S uv.1, have Au := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.1; have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy ah.2 uv.2; have Moh := (M uv.1).outcome ah.1; strategy.state.ev (strategy.state.opTensor (Au * Moh * (Av - Au)) (T.outcome ah.2))

Reverse-orientation selected form of addInU_selected_cs_chain_step4_diff_eq.

theorem MIPRE.LIDT.Co.SelfImprovement.addInU_cs_chain_step4_diff_eq {๐”“ : Type u_1} [CStarAlgebra ๐”“] [PartialOrder ๐”“] [StarOrderedRing ๐”“] {K : Type u_2} [NormedAddCommGroup K] [InnerProductSpace โ„‚ K] [CompleteSpace K] (params : MIPStarRE.LDT.Parameters) [MIPStarRE.LDT.FieldModel params.q] (strategy : SymStrat params ๐”“ K) (T : SubMeas (MIPStarRE.LDT.Polynomial params) ๐”“) :
addInUCSChainQ3 params strategy T - addInUCSChainQ4 params strategy T = MIPStarRE.LDT.avgOver (MIPStarRE.LDT.uniformDistribution (MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params)) fun (uv : MIPStarRE.LDT.Point params ร— MIPStarRE.LDT.Point params) => โˆ‘ h : MIPStarRE.LDT.Polynomial params, have Au := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.1; have Av := GlobalVariance.pointConditionedOutcomeOperatorAtPolynomial params strategy h uv.2; have Mh := (sandwichedPolynomialSubMeasAt params strategy T uv.1).outcome h; strategy.state.ev (strategy.state.opTensor (Au * Mh * (Av - Au)) (T.outcome h))

Algebraic CS-alignment for the Qโ‚ƒ โ†’ Qโ‚„ step.

Rewrites the difference addInUCSChainQ3 - addInUCSChainQ4 in the exact form appearing on the left-hand side of eq:change-another (paper lines 326--332): the expectation of (A^u_{h(u)} ยท H^u_h ยท (A^v_{h(v)} - A^u_{h(u)})) โŠ— T_h, averaged over (u, v) and summed over h.

This identity is purely algebraic; the actual operator Cauchy--Schwarz step is provided by add_in_u_cs_chain_q3_q4_factored_cs.