To　ensure　the　system　stability　of　the　H2\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\mathcal　{H}_{2}$$\end{document}-guaranteed　cost　optimal　decentralized　control　(ODC)　problem,　we　formulate　an　approximate　semidefinite　programming　(SDP)　problem　that　leverages　the　block　diagonal　structure　of　the　decentralized　controller＇s　gain　matrix.　To　minimize　data　storage　requirements　and　enhance　computational　efficiency,　we　employ　the　Kronecker　product　to　vectorize　the　SDP　problem　into　a　conic　programming　(CP)　problem.　We　then　propose　a　proximal　alternating　direction　method　of　multipliers　(PADMM)　to　solve　the　dual　of　the　resulting　CP　problem.　By　using　the　equivalence　between　the　semi-proximal　ADMM　and　the　(partial)　proximal　point　algorithm,　we　identify　the　non-expansive　operator　of　PADMM,　enabling　the　use　of　Halpern　fixed-point　iteration　to　accelerate　the　algorithm.　Finally,　we　demonstrate　that　the　sequence　generated　by　the　proposed　accelerated　PADMM　exhibits　a　fast　convergence　rate　for　the　Karush-Kuhn-Tucker　residual.　Numerical　experiments　confirm　that　the　accelerated　algorithm　outperforms　the　well-known　COSMO,　MOSEK,　and　SCS　solvers　in　efficiently　solving　large-scale　CP　problems,　particularly　those　arising　from　H2\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\mathcal　{H}_{2}$$\end{document}-guaranteed　cost　ODC　problems.