Sandwiched Aux Fun concave in H
sandwichedAuxFun_concave_in_H
Plain-language statement
For α > 1, the map H ↦ f_α(H, ρ, σ) is concave (for fixed ρ, σ), so the optimal H is a maximizer.
Exact Lean statement
lemma sandwichedAuxFun_concave_in_H (hα : 1 < α) (ρ σ : MState d) :
ConcaveOn ℝ {H | 0 ≤ H} (fun H => f_alpha α H ρ σ)Formal artifact
Lean source
lemma sandwichedAuxFun_concave_in_H (hα : 1 < α) (ρ σ : MState d) : ConcaveOn ℝ {H | 0 ≤ H} (fun H => f_alpha α H ρ σ) := by constructor · rw [convex_iff_forall_pos] exact fun x hx y hy a b ha hb hab => by simpa [ hab ] using add_nonneg ( smul_nonneg ha.le hx ) ( smul_nonneg hb.le hy ) ; · intro x hx y hy a b ha hb hab simp [f_alpha]; -- Apply the convexity of the trace function composed with rpow. have h_trace_convex : ConvexOn ℝ {A : HermitianMat d ℂ | 0 ≤ A} (fun A => (A ^ (α / (α - 1))).trace) := by have h_trace_convex : ConvexOn ℝ (Set.Ici 0) (fun x : ℝ => x ^ (α / (α - 1))) := by exact ( convexOn_rpow ( by rw [ le_div_iff₀ ] <;> linarith ) ); convert HermitianMat.trace_function_convex_ici h_trace_convex using 1; have := h_trace_convex.2 ( show 0 ≤ ( HermitianMat.conj ( σ.M ^ ( - ( ( 1 - α ) / ( 2 * α ) ) ) ) x ) from ?_ ) ( show 0 ≤ ( HermitianMat.conj ( σ.M ^ ( - ( ( 1 - α ) / ( 2 * α ) ) ) ) y ) from ?_ ) ha hb hab; · simp_all +decide [ inner_add_right, inner_smul_right, HermitianMat.conj ]; nlinarith! [ show 0 ≤ α - 1 by linarith ]; · apply_rules [ HermitianMat.conj_nonneg ]; · apply_rules [ HermitianMat.conj_nonneg ]- Project
- quantumInfo
- License
- MIT
- Commit
- 56e83a9288a3
- Source
- QuantumInfo/Finite/Entropy/DPI.lean:469-485
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Project documentation
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Source project: quantumInfo
Person-level attribution pending.
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convex_roof_of_pure
Plain-language statement
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Source project: quantumInfo
Person-level attribution pending.
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CPTPMap.id_achievesRate_log_dim
Plain-language statement
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Source project: quantumInfo
Person-level attribution pending.