Fast Distributed Gradient Methods
We study distributed optimization problems when N nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant L), and bounded gradient. We propose two fast distributed gradient algorithms based on the c...
Uloženo v:
| Vydáno v: | IEEE transactions on automatic control Ročník 59; číslo 5; s. 1131 - 1146 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.05.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0018-9286, 1558-2523 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | We study distributed optimization problems when N nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant L), and bounded gradient. We propose two fast distributed gradient algorithms based on the centralized Nesterov gradient algorithm and establish their convergence rates in terms of the per-node communications K and the per-node gradient evaluations k. Our first method, Distributed Nesterov Gradient, achieves rates O( logK/K) and O(logk/k). Our second method, Distributed Nesterov gradient with Consensus iterations, assumes at all nodes knowledge of L and μ(W) - the second largest singular value of the N ×N doubly stochastic weight matrix W. It achieves rates O( 1/ K 2-ξ ) and O( 1/k 2 ) ( ξ > 0 arbitrarily small). Further, we give for both methods explicit dependence of the convergence constants on N and W. Simulation examples illustrate our findings. |
|---|---|
| AbstractList | We study distributed optimization problems when N nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant L), and bounded gradient. We propose two fast distributed gradient algorithms based on the centralized Nesterov gradient algorithm and establish their convergence rates in terms of the per-node communications K and the per-node gradient evaluations k. Our first method, Distributed Nesterov Gradient, achieves rates O( logK/K) and O(logk/k). Our second method, Distributed Nesterov gradient with Consensus iterations, assumes at all nodes knowledge of L and μ(W) - the second largest singular value of the N ×N doubly stochastic weight matrix W. It achieves rates O( 1/ K 2-ξ ) and O( 1/k 2 ) ( ξ > 0 arbitrarily small). Further, we give for both methods explicit dependence of the convergence constants on N and W. Simulation examples illustrate our findings. We study distributed optimization problems when [Formula Omitted] nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant [Formula Omitted]), and bounded gradient. We propose two fast distributed gradient algorithms based on the centralized Nesterov gradient algorithm and establish their convergence rates in terms of the per-node communications [Formula Omitted] and the per-node gradient evaluations [Formula Omitted]. Our first method, Distributed Nesterov Gradient, achieves rates [Formula Omitted] and [Formula Omitted]. Our second method, Distributed Nesterov gradient with Consensus iterations, assumes at all nodes knowledge of [Formula Omitted] and [Formula Omitted] - the second largest singular value of the [Formula Omitted] doubly stochastic weight matrix [Formula Omitted]. It achieves rates [Formula Omitted] and [Formula Omitted] ([Formula Omitted] arbitrarily small). Further, we give for both methods explicit dependence of the convergence constants on [Formula Omitted] and [Formula Omitted]. Simulation examples illustrate our findings. We study distributed optimization problems when N nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex, have Lipschitz continuous gradient (with constant L ), and bounded gradient. We propose two fast distributed gradient algorithms based on the centralized Nesterov gradient algorithm and establish their convergence rates in terms of the per-node communications calK and the per-node gradient evaluations k . Our first method, Distributed Nesterov Gradient, achieves rates syntax error at token { and syntax error at token { . Our second method, Distributed Nesterov gradient with Consensus iterations, assumes at all nodes knowledge of L and mu openbracket W [ closebracket ] - the second largest singular value of the N timesN doubly stochastic weight matrix W . It achieves rates syntax error at token { and syntax error at token { ( xi > 0 arbitrarily small). Further, we give for both methods explicit dependence of the convergence constants on N and W . Simulation examples illustrate our findings. |
| Author | Xavier, Joao Moura, Jose M. F. Jakovetic, Dusan |
| Author_xml | – sequence: 1 givenname: Dusan surname: Jakovetic fullname: Jakovetic, Dusan email: djakovet@uns.ac.rs organization: Inst. for Syst. & Robot., Univ. of Lisbon, Lisbon, Portugal – sequence: 2 givenname: Joao surname: Xavier fullname: Xavier, Joao email: jxavier@isr.ist.utl.pt organization: Inst. de Sist. e Robot. (ISR), Univ. of Lisbon, Lisbon, Portugal – sequence: 3 givenname: Jose M. F. surname: Moura fullname: Moura, Jose M. F. email: moura@ece.cmu.edu organization: Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA |
| BookMark | eNp9kD1PwzAQQC1UJNrCjsRSxMKSYp8_koxVoAWpiKXMluNchKs0KbYz8O9J1YqhA5N10nun85uQUdu1SMgto3PGaP60WRRzoEzMAfIsZXBBxkzKLAEJfETGlLIsySFTV2QSwnYYlRBsTO6XJsTZswvRu7KPWM1W3lQO2zh7x_jVVeGaXNamCXhzeqfkc_myKV6T9cfqrVisE8tBxETVAhAVBVmKsuJgqbC0LBVnQqZgGNYGLLdlihW1yqZorZSpkiXDVAiKfEoej3v3vvvuMUS9c8Fi05gWuz5opobvUc45G9CHM3Tb9b4drtNMCpnlABkdKHWkrO9C8Fhr66KJrmujN67RjOpDOT2U04dy-lRuEOmZuPduZ_zPf8rdUXGI-IerlEoFkv8CEOl4TA |
| CODEN | IETAA9 |
| CitedBy_id | crossref_primary_10_1109_TAC_2017_2737582 crossref_primary_10_1109_TAC_2019_2894588 crossref_primary_10_1109_TAC_2020_2989266 crossref_primary_10_1109_TAC_2017_2743462 crossref_primary_10_1016_j_energy_2021_119898 crossref_primary_10_1109_TCNS_2023_3237496 crossref_primary_10_1109_TWC_2014_2364573 crossref_primary_10_1109_TSP_2016_2617829 crossref_primary_10_1109_TSP_2021_3094906 crossref_primary_10_1109_TSIPN_2022_3188450 crossref_primary_10_1137_18M122902X crossref_primary_10_1109_TCNS_2025_3526343 crossref_primary_10_1016_j_neucom_2022_01_015 crossref_primary_10_1109_TCNS_2022_3165022 crossref_primary_10_1109_TIT_2023_3267742 crossref_primary_10_1109_TKDE_2024_3491778 crossref_primary_10_1137_20M1340071 crossref_primary_10_1016_j_cam_2024_116383 crossref_primary_10_1109_TAC_2024_3525182 crossref_primary_10_3390_math11234771 crossref_primary_10_1007_s11424_021_9355_5 crossref_primary_10_1007_s11768_015_5100_8 crossref_primary_10_1109_TAC_2022_3152722 crossref_primary_10_1109_TII_2020_2966713 crossref_primary_10_1109_TSP_2019_2951216 crossref_primary_10_1007_s10107_018_1355_4 crossref_primary_10_1007_s10107_023_01997_7 crossref_primary_10_1109_TAC_2014_2363299 crossref_primary_10_1109_TAC_2022_3152720 crossref_primary_10_1109_TAC_2020_2973583 crossref_primary_10_1109_TKDE_2020_2989393 crossref_primary_10_1016_j_asoc_2025_113041 crossref_primary_10_1016_j_automatica_2022_110569 crossref_primary_10_1111_wre_12255 crossref_primary_10_1109_TAC_2021_3126253 crossref_primary_10_1007_s10589_022_00432_5 crossref_primary_10_1007_s10589_019_00131_8 crossref_primary_10_1016_j_automatica_2019_03_015 crossref_primary_10_1109_TASLP_2022_3224256 crossref_primary_10_1109_TAC_2018_2797164 crossref_primary_10_1109_TAC_2022_3154356 crossref_primary_10_1016_j_arcontrol_2019_05_006 crossref_primary_10_1109_TAC_2016_2612819 crossref_primary_10_1137_14096668X crossref_primary_10_1016_j_comcom_2022_12_012 crossref_primary_10_1080_00207721_2020_1815098 crossref_primary_10_1109_TAC_2020_2989282 crossref_primary_10_1109_TSP_2016_2548989 crossref_primary_10_1137_16M1081257 crossref_primary_10_1109_TCNS_2015_2476198 crossref_primary_10_1016_j_sysconle_2022_105265 crossref_primary_10_1080_00207721_2018_1442886 crossref_primary_10_1109_MSP_2020_2975210 crossref_primary_10_1109_TAC_2016_2615066 crossref_primary_10_1109_TAC_2025_3546087 crossref_primary_10_1109_TSIPN_2018_2806841 crossref_primary_10_1109_TAC_2022_3221856 crossref_primary_10_1109_OJCOMS_2025_3566464 crossref_primary_10_1109_TSMC_2019_2963452 crossref_primary_10_1109_TSIPN_2021_3087110 crossref_primary_10_1109_TCYB_2023_3270185 crossref_primary_10_1109_TAC_2020_3008713 crossref_primary_10_1109_TSP_2019_2894803 crossref_primary_10_1109_TSIPN_2018_2806844 crossref_primary_10_1016_j_sysconle_2016_02_010 crossref_primary_10_1109_TCYB_2015_2464255 crossref_primary_10_1186_s13634_021_00736_4 crossref_primary_10_1049_cth2_12062 crossref_primary_10_1049_iet_cta_2019_0708 crossref_primary_10_1109_TAC_2017_2763782 crossref_primary_10_1109_JPROC_2020_3007634 crossref_primary_10_1109_TCYB_2022_3218663 crossref_primary_10_1007_s10957_021_01833_y crossref_primary_10_1109_ACCESS_2023_3297112 crossref_primary_10_1080_00207721_2025_2530672 crossref_primary_10_1109_JIOT_2020_3040657 crossref_primary_10_1016_j_automatica_2021_109739 crossref_primary_10_1007_s11075_023_01650_6 crossref_primary_10_1109_TAC_2017_2730481 crossref_primary_10_1109_TAC_2021_3116116 crossref_primary_10_1007_s10957_023_02336_8 crossref_primary_10_1016_j_ifacsc_2018_01_001 crossref_primary_10_1109_TAC_2019_2937496 crossref_primary_10_1016_j_disopt_2020_100595 crossref_primary_10_1109_TASLP_2021_3058539 crossref_primary_10_1109_TSP_2015_2436358 crossref_primary_10_1016_j_sysconle_2024_105733 crossref_primary_10_1137_130943170 crossref_primary_10_1109_TAC_2021_3137274 crossref_primary_10_1016_j_ijepes_2020_106240 crossref_primary_10_3390_s20216153 crossref_primary_10_1007_s11590_023_02011_x crossref_primary_10_1049_sil2_12199 crossref_primary_10_1109_JSAC_2023_3244250 crossref_primary_10_1007_s11075_019_00746_2 crossref_primary_10_1016_j_jfranklin_2023_08_015 crossref_primary_10_1109_TSP_2017_2666776 crossref_primary_10_1142_S0217595919500234 crossref_primary_10_1109_TCNS_2017_2776084 crossref_primary_10_1016_j_automatica_2017_06_011 crossref_primary_10_1109_TSP_2016_2602803 crossref_primary_10_1016_j_jfranklin_2019_03_021 crossref_primary_10_1109_TSP_2023_3250839 crossref_primary_10_1109_TNSE_2022_3153386 crossref_primary_10_1109_TNNLS_2022_3192346 crossref_primary_10_3390_biomimetics10080496 crossref_primary_10_1109_MRA_2024_3352852 crossref_primary_10_1109_TAC_2025_3546096 crossref_primary_10_1007_s10957_021_01982_0 crossref_primary_10_1007_s10957_024_02595_z crossref_primary_10_1016_j_sysconle_2016_05_011 crossref_primary_10_1049_trit_2020_0082 crossref_primary_10_1080_23335777_2015_1062049 crossref_primary_10_1109_TCYB_2017_2755720 crossref_primary_10_1109_TSIPN_2016_2612120 crossref_primary_10_1515_auto_2019_0064 crossref_primary_10_1109_TAC_2019_2901829 crossref_primary_10_1109_TSP_2018_2818081 crossref_primary_10_1109_TAC_2022_3174187 crossref_primary_10_1109_TCSII_2021_3132392 crossref_primary_10_1109_TSMC_2019_2936829 crossref_primary_10_1137_15M1048896 crossref_primary_10_1016_j_neucom_2021_06_097 crossref_primary_10_1109_TAC_2024_3413854 crossref_primary_10_1109_TSIPN_2018_2846183 crossref_primary_10_1109_TAC_2017_2714645 crossref_primary_10_1007_s11424_023_1321_y crossref_primary_10_1016_j_automatica_2020_109407 crossref_primary_10_1109_TAC_2018_2810511 crossref_primary_10_1109_TAC_2023_3301289 crossref_primary_10_1109_JPROC_2018_2817461 crossref_primary_10_1109_TAC_2022_3173062 crossref_primary_10_1109_TAC_2022_3201141 crossref_primary_10_1080_17445760_2019_1577416 crossref_primary_10_1049_cth2_12022 crossref_primary_10_1137_16M1084316 crossref_primary_10_1109_JSTSP_2012_2233193 crossref_primary_10_1109_TAC_2022_3185178 crossref_primary_10_1016_j_egyr_2020_11_163 crossref_primary_10_1109_LCSYS_2024_3400699 crossref_primary_10_1109_LCSYS_2025_3577964 crossref_primary_10_1109_TAC_2024_3519938 crossref_primary_10_1109_TSIPN_2016_2620440 crossref_primary_10_1109_TAC_2022_3160684 crossref_primary_10_1109_TAC_2017_2677879 crossref_primary_10_1109_TSP_2019_2926022 crossref_primary_10_1016_j_automatica_2018_10_007 crossref_primary_10_1016_j_jfranklin_2019_07_018 crossref_primary_10_1109_TNNLS_2021_3110295 crossref_primary_10_1109_TPAMI_2022_3196503 crossref_primary_10_1016_j_automatica_2018_04_010 crossref_primary_10_1016_j_jfranklin_2023_12_030 crossref_primary_10_1016_j_sigpro_2022_108748 crossref_primary_10_1007_s12555_024_0424_0 crossref_primary_10_1109_TSIPN_2017_2662618 crossref_primary_10_3390_s19020301 crossref_primary_10_1109_LCSYS_2017_2698179 crossref_primary_10_1016_j_jai_2023_06_003 crossref_primary_10_1137_21M143546X crossref_primary_10_1007_s10107_018_01357_w crossref_primary_10_1080_10556788_2023_2280062 crossref_primary_10_1109_TSG_2022_3201814 crossref_primary_10_1016_j_automatica_2019_108755 crossref_primary_10_3390_en15114020 crossref_primary_10_1109_TNSE_2021_3122927 crossref_primary_10_1109_TAC_2017_2672698 crossref_primary_10_1109_TSP_2023_3336175 crossref_primary_10_1109_TAC_2014_2298712 crossref_primary_10_1109_TAC_2021_3111853 crossref_primary_10_1007_s11768_019_8179_5 crossref_primary_10_1109_ACCESS_2019_2960494 crossref_primary_10_1007_s10898_024_01418_9 crossref_primary_10_1109_TNSE_2020_2965999 crossref_primary_10_1007_s11633_022_1319_2 crossref_primary_10_1002_asjc_2489 crossref_primary_10_1049_ell2_13313 crossref_primary_10_1109_TSP_2021_3097211 crossref_primary_10_1109_TCYB_2020_3022240 crossref_primary_10_1109_TSP_2024_3366437 crossref_primary_10_1109_TSP_2013_2291221 crossref_primary_10_1016_j_epsr_2022_108630 crossref_primary_10_1109_TSIPN_2024_3379844 crossref_primary_10_1109_TSP_2023_3262181 crossref_primary_10_1016_j_automatica_2017_05_004 crossref_primary_10_1109_TSP_2021_3083981 crossref_primary_10_1109_JBHI_2022_3140455 crossref_primary_10_1109_TETCI_2021_3098831 crossref_primary_10_1109_TCNS_2021_3050129 crossref_primary_10_1109_TSP_2023_3297053 crossref_primary_10_1016_j_neucom_2024_127584 crossref_primary_10_1109_TCNS_2022_3213706 crossref_primary_10_1109_TCNS_2016_2573639 crossref_primary_10_1007_s40747_022_00785_8 crossref_primary_10_1109_TAC_2020_2987379 crossref_primary_10_1109_TCNS_2018_2809963 crossref_primary_10_1007_s10898_025_01506_4 crossref_primary_10_1109_TNSE_2023_3321757 crossref_primary_10_1109_LCSYS_2019_2920342 crossref_primary_10_1109_JAS_2024_124695 crossref_primary_10_1109_TAC_2017_2685559 crossref_primary_10_1016_j_jpdc_2022_03_014 crossref_primary_10_1016_j_automatica_2019_108538 crossref_primary_10_1146_annurev_control_060117_105131 crossref_primary_10_1186_s13634_018_0596_y crossref_primary_10_1109_TAC_2018_2849616 crossref_primary_10_1109_TAC_2021_3131678 crossref_primary_10_1109_TCAD_2020_3013072 crossref_primary_10_1007_s00521_023_09067_x crossref_primary_10_1109_TSP_2019_2901355 crossref_primary_10_1109_TSP_2020_2997394 crossref_primary_10_1137_15M1038049 crossref_primary_10_1109_TSP_2021_3092347 crossref_primary_10_1016_j_sysconle_2018_08_002 crossref_primary_10_1109_TSP_2019_2943230 crossref_primary_10_1016_j_ins_2020_05_119 crossref_primary_10_1109_TSIPN_2020_2967143 crossref_primary_10_1016_j_epsr_2024_111007 crossref_primary_10_1109_TNSE_2019_2933177 crossref_primary_10_1109_TSIPN_2016_2524588 crossref_primary_10_1002_asjc_3422 crossref_primary_10_1109_TAC_2019_2933607 crossref_primary_10_1016_j_automatica_2024_111596 crossref_primary_10_1109_TSIPN_2022_3203860 crossref_primary_10_1109_TCYB_2020_2989835 crossref_primary_10_1109_TAC_2018_2805260 crossref_primary_10_1137_18M119046X crossref_primary_10_1007_s10589_025_00666_z crossref_primary_10_1109_TSMC_2019_2946287 crossref_primary_10_1109_TSP_2018_2793878 crossref_primary_10_1109_TAC_2023_3275927 crossref_primary_10_1007_s00180_022_01242_3 crossref_primary_10_1016_j_neunet_2024_106123 crossref_primary_10_1109_TAC_2015_2414771 crossref_primary_10_1109_TSIPN_2023_3239698 crossref_primary_10_1109_TSP_2020_3018317 crossref_primary_10_1109_JAS_2021_1003904 crossref_primary_10_1002_asjc_2284 crossref_primary_10_1016_j_jobe_2019_101044 crossref_primary_10_1109_TSP_2025_3531292 crossref_primary_10_1007_s10489_023_04765_4 crossref_primary_10_1007_s10898_024_01395_z crossref_primary_10_1109_TCSII_2023_3324187 crossref_primary_10_1016_j_sigpro_2020_107614 crossref_primary_10_1016_j_sigpro_2024_109512 crossref_primary_10_3390_s20051302 crossref_primary_10_1109_TCNS_2019_2925267 crossref_primary_10_1109_TCNS_2017_2698261 crossref_primary_10_1109_TCYB_2020_3011819 crossref_primary_10_1016_j_sysconle_2016_07_009 crossref_primary_10_1016_j_asoc_2023_110513 crossref_primary_10_1109_JSAC_2021_3118428 crossref_primary_10_1007_s42979_021_00463_5 crossref_primary_10_1016_j_compchemeng_2024_108778 crossref_primary_10_1109_TSIPN_2021_3083975 crossref_primary_10_1137_22M148570X crossref_primary_10_1109_TAC_2020_2996205 crossref_primary_10_1109_TAC_2022_3207866 crossref_primary_10_1016_j_ins_2015_01_007 crossref_primary_10_1109_TETCI_2024_3437249 crossref_primary_10_1360_SSI_2025_0112 crossref_primary_10_1049_iet_cta_2018_6224 crossref_primary_10_1109_TCNS_2020_3005079 crossref_primary_10_1109_TMC_2024_3502574 crossref_primary_10_1016_j_automatica_2020_108962 crossref_primary_10_1016_j_ifacol_2023_10_100 crossref_primary_10_1109_TSP_2023_3277211 crossref_primary_10_1109_TASLP_2020_3037532 crossref_primary_10_1109_TSMC_2021_3112691 crossref_primary_10_1016_j_chaos_2025_117103 crossref_primary_10_1049_iet_spr_2018_0033 crossref_primary_10_1109_TSMC_2023_3331334 crossref_primary_10_1109_TSP_2019_2932876 crossref_primary_10_1109_TCOMM_2020_3026398 crossref_primary_10_1109_TSP_2017_2669896 crossref_primary_10_1016_j_ijepes_2021_107373 crossref_primary_10_1016_j_jfranklin_2025_107611 crossref_primary_10_1109_TSP_2020_2981204 crossref_primary_10_1109_TSP_2016_2560133 crossref_primary_10_1109_TSP_2023_3240083 crossref_primary_10_1007_s10107_020_01487_0 crossref_primary_10_1109_LSP_2018_2841648 crossref_primary_10_1109_TAC_2021_3095456 crossref_primary_10_1109_TSP_2023_3343561 crossref_primary_10_1080_10556788_2020_1712599 crossref_primary_10_1109_TSMC_2019_2960770 crossref_primary_10_1109_TSP_2017_2673815 crossref_primary_10_1016_j_conengprac_2023_105728 crossref_primary_10_1016_j_ins_2017_09_038 crossref_primary_10_1109_TSP_2025_3533208 crossref_primary_10_3390_math10030357 crossref_primary_10_1007_s11424_021_1231_9 crossref_primary_10_1109_TCNS_2020_3011837 crossref_primary_10_1002_asjc_3467 crossref_primary_10_1007_s13042_025_02609_w crossref_primary_10_1109_TAC_2018_2880407 crossref_primary_10_1109_TSIPN_2019_2957719 crossref_primary_10_1016_j_jfranklin_2019_01_050 crossref_primary_10_1109_TAC_2017_2713046 crossref_primary_10_1093_imanum_drz027 crossref_primary_10_1007_s10898_022_01221_4 crossref_primary_10_1007_s10957_020_01648_3 crossref_primary_10_1109_TSIPN_2017_2681208 crossref_primary_10_1002_oca_2973 crossref_primary_10_1109_TCYB_2021_3099905 crossref_primary_10_1109_TAC_2016_2525808 crossref_primary_10_1109_TAC_2022_3179216 crossref_primary_10_1109_TSP_2013_2295055 crossref_primary_10_1109_TSMC_2018_2823901 crossref_primary_10_1016_j_automatica_2022_110260 |
| Cites_doi | 10.1109/TSP.2011.2146776 10.1109/CDC.2012.6425938 10.1109/ACC.2012.6314986 10.1007/s10107-013-0677-5 10.1561/2200000016 10.1016/j.automatica.2007.09.003 10.1145/1007352.1007438 10.1109/TAC.2008.2007159 10.1109/TAC.2011.2161027 10.1137/090770102 10.1109/TSP.2010.2055862 10.1109/ICASSP.2011.5946267 10.1109/TIT.2006.874516 10.1109/TAC.2011.2167817 10.1109/Allerton.2012.6483273 10.1137/040615961 10.1109/TAC.2014.2298712 10.1109/Allerton.2011.6120272 10.1109/ACC.2011.5990992 10.1109/JSTSP.2011.2120593 10.1145/984622.984626 10.1109/TAC.1986.1104412 10.1109/ALLERTON.2008.4797579 10.1109/TAC.2008.2009515 10.1109/TSP.2011.2182347 10.1109/TSP.2012.2198470 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) May 2014 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) May 2014 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D F28 |
| DOI | 10.1109/TAC.2014.2298712 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitleList | Technology Research Database Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-2523 |
| EndPage | 1146 |
| ExternalDocumentID | 3377140221 10_1109_TAC_2014_2298712 6705625 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: AFOSR grantid: FA95501010291 – fundername: National Science Foundation (NSF) grantid: CCF1011903 – fundername: FCT grantid: CMU-PT/SIA/0026/2009; PTDC/EMS-CRO/2042/2012; SFRH/BD/33518/2008 – fundername: ISR/IST plurianual funding (POSC program, FEDER) – fundername: Carnegie Mellon|Portugal Program under a grant from the Fundação de Ci¿ncia e Tecnologia (FCT) from Portugal |
| GroupedDBID | -~X .DC 0R~ 29I 3EH 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 VJK ~02 AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D RIG F28 |
| ID | FETCH-LOGICAL-c324t-6f42ee6025b4bd32c04c0bb6314572a1efa2c3cb7ed0c6c7ecc55765b1e7440e3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 458 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000335218900002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0018-9286 |
| IngestDate | Thu Oct 02 10:08:05 EDT 2025 Mon Jun 30 10:09:21 EDT 2025 Sat Nov 29 02:26:01 EST 2025 Tue Nov 18 20:48:39 EST 2025 Wed Aug 27 02:51:59 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c324t-6f42ee6025b4bd32c04c0bb6314572a1efa2c3cb7ed0c6c7ecc55765b1e7440e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 1545892280 |
| PQPubID | 85475 |
| PageCount | 16 |
| ParticipantIDs | proquest_journals_1545892280 crossref_citationtrail_10_1109_TAC_2014_2298712 crossref_primary_10_1109_TAC_2014_2298712 ieee_primary_6705625 proquest_miscellaneous_1620103331 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-05-01 |
| PublicationDateYYYYMMDD | 2014-05-01 |
| PublicationDate_xml | – month: 05 year: 2014 text: 2014-05-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on automatic control |
| PublicationTitleAbbrev | TAC |
| PublicationYear | 2014 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref35 ref13 ref37 ref15 ref36 ref14 zhu (ref16) 2012; 57 shanbhag (ref24) 2011; 21 ref33 ref11 ref32 ref10 ref1 ghadimi (ref27) 2012 ref19 xiao (ref28) 2005 chen (ref20) 2012 ref18 terelius (ref25) 2011 ram (ref9) 2011; 147 tsitsiklis (ref2) 1984 tseng (ref30) 0 ref23 ref26 shi (ref34) 2012 ref22 ref21 nesterov (ref17) 1983; 269 ref29 nesterov (ref38) 2007 ref8 ref7 ref4 ref3 ref6 ref5 lopes (ref12) 2006 vandenberghe (ref31) 2010 |
| References_xml | – year: 2006 ident: ref12 article-title: Adaptive estimation algorithms over distributed networks publication-title: Proc 21st IEICE Signal Process Symp – ident: ref21 doi: 10.1109/TSP.2011.2146776 – ident: ref32 doi: 10.1109/CDC.2012.6425938 – ident: ref37 doi: 10.1109/ACC.2012.6314986 – volume: 269 start-page: 543 year: 1983 ident: ref17 article-title: A method for solving the convex programming problem with convergence rate <formula formulatype="inline"><tex Notation="TeX">${\rm O}(1/k^{2})$</tex></formula> publication-title: Dokl Akad Nauk SSSR – ident: ref33 doi: 10.1007/s10107-013-0677-5 – ident: ref7 doi: 10.1561/2200000016 – ident: ref4 doi: 10.1016/j.automatica.2007.09.003 – ident: ref35 doi: 10.1145/1007352.1007438 – year: 2012 ident: ref27 publication-title: Accelerated Gradient Methods for Networked Optimization – ident: ref6 doi: 10.1109/TAC.2008.2007159 – ident: ref14 doi: 10.1109/TAC.2011.2161027 – volume: 21 start-page: 1046 year: 2011 ident: ref24 article-title: Multiuser optimization: distributed algorithms and error analysis publication-title: SIAM J Control Optim doi: 10.1137/090770102 – year: 0 ident: ref30 article-title: On accelerated proximal-gradient methods for convex-concave optimization publication-title: SIAM J Optim – start-page: 7391 year: 2011 ident: ref25 article-title: Decentralized multi-agent optimization via dual decomposition publication-title: Proc 18th World Congr o Int Fed Autom Control (IFAC) – ident: ref5 doi: 10.1109/TSP.2010.2055862 – ident: ref22 doi: 10.1109/ICASSP.2011.5946267 – ident: ref36 doi: 10.1109/TIT.2006.874516 – year: 2010 ident: ref31 publication-title: Optimization Methods for Large-Scale Systems – volume: 57 start-page: 151 year: 2012 ident: ref16 article-title: On distributed convex optimization under inequality and equality constraints publication-title: IEEE Trans Autom Control doi: 10.1109/TAC.2011.2167817 – ident: ref19 doi: 10.1109/Allerton.2012.6483273 – ident: ref29 doi: 10.1137/040615961 – ident: ref18 doi: 10.1109/TAC.2014.2298712 – year: 2012 ident: ref20 publication-title: Fast Distributed First-Order Methods – ident: ref15 doi: 10.1109/Allerton.2011.6120272 – ident: ref26 doi: 10.1109/ACC.2011.5990992 – year: 2007 ident: ref38 publication-title: ?Gradient Methods for Minimizing Composite Objective Function ? – ident: ref11 doi: 10.1109/JSTSP.2011.2120593 – year: 1984 ident: ref2 publication-title: Problems in decentralized decision making and computation – start-page: 63 year: 2005 ident: ref28 article-title: A scheme for robust distributed sensor fusion based on average consensus publication-title: Proc Inf Process Sens Netw (IPSN'05) – ident: ref3 doi: 10.1145/984622.984626 – ident: ref1 doi: 10.1109/TAC.1986.1104412 – year: 2012 ident: ref34 publication-title: ?Finite-Time and Asymptotic Convergence of Distributed Averaging and Maximizing Algorithms ? – ident: ref10 doi: 10.1109/ALLERTON.2008.4797579 – volume: 147 start-page: 516 year: 2011 ident: ref9 article-title: Distributed stochastic subgradient projection algorithms for convex optimization publication-title: J Optim Theory Appl – ident: ref8 doi: 10.1109/TAC.2008.2009515 – ident: ref23 doi: 10.1109/TSP.2011.2182347 – ident: ref13 doi: 10.1109/TSP.2012.2198470 |
| SSID | ssj0016441 |
| Score | 2.6273267 |
| Snippet | We study distributed optimization problems when N nodes minimize the sum of their individual costs subject to a common vector variable. The costs are convex,... We study distributed optimization problems when [Formula Omitted] nodes minimize the sum of their individual costs subject to a common vector variable. The... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1131 |
| SubjectTerms | Acceleration Algorithm design and analysis Algorithms Consensus Constants Convergence convergence rate Costs distributed optimization Educational institutions Errors Gradient methods Mathematical analysis Nesterov gradient Optimization Syntax Vectors |
| Title | Fast Distributed Gradient Methods |
| URI | https://ieeexplore.ieee.org/document/6705625 https://www.proquest.com/docview/1545892280 https://www.proquest.com/docview/1620103331 |
| Volume | 59 |
| WOSCitedRecordID | wos000335218900002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 1558-2523 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016441 issn: 0018-9286 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED624YM--GuK1Skd-CLYrU3SpH0c0-mLw4cJeytNcgVBNtk6_36TtCuKIvhWaNqU73q5u9zlO4DrpIg1RjwMBJdxwCQ3KsWVCVYiHqeF1DoX2jWbENNpMp-nzy24bc7CIKIrPsOBvXS5fL1UG7tVNuTCmuu4DW0heHVWq8kYWLterbpmNpI0KckwHc5GY1vDxQaEmAg7It9MkOup8mMhdtZlcvC_7zqE_dqL9EeV2I-ghYtj2PvCLdiF_iRfl_6d5cW1La1Q-w8rV95V-k-ua_T6BF4m97PxY1D3QwiUcXvKgBeMIHLjpUgmNSUqZCqUktOIxYLkERY5UVRJgTpUXAkjndiEE7GM0NIAIj2FzmK5wDPwjZtUWCo6xlTKKAqpE5GLEFOV0oQnxIPhFqJM1WThtmfFW-aChjDNDKiZBTWrQfXgpnnivSLK-GNs14LYjKvx86C3lUJWa9I6c5m91JL2eNBvbhsdsImNfIHLjRnDbU6fUhqd__7mC9i181eFij3olKsNXsKO-ihf16sr9yN9AiW9wss |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mB6gPfovTqR34ItitTdKkfRzTqeiGDxN8K01yBUE22Tr_fpO0FkURfCs0bcPverm73OV3AOdxHmkMeeALLiOfSW5UiisTrIQ8SnKpdSa0azYhRqP4-Tl5bMBlfRYGEV3xGXbspcvl66la2K2yLhfWXEdLsBIxRoLytFadM7CWvVx3zfdIXCclg6Q77vVtFRfrEGJi7JB8M0Kuq8qPpdjZl8HW_2a2DZuVH-n1SsHvQAMnu7DxhV1wD9qDbF54V5YZ1za1Qu3dzFyBV-ENXd_o-T48Da7H_Vu_6ojgK-P4FD7PGUHkxk-RTGpKVMBUICWnIYsEyULMM6KokgJ1oLgSRj6RCSgiGaIlAkR6AMuT6QQPwTOOUm7J6BhTCaMopI5FJgJMVEJjHpMmdD8hSlVFF267VrymLmwIktSAmlpQ0wrUJlzUT7yVVBl_jN2zINbjKvya0PqUQlrp0jx1ub3E0vY0oV3fNlpgUxvZBKcLM4bbrD6lNDz6_c1nsHY7Hj6kD3ej-2NYt3MpyxZbsFzMFngCq-q9eJnPTt1P9QF2xsYS |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fast+Distributed+Gradient+Methods&rft.jtitle=IEEE+transactions+on+automatic+control&rft.au=Jakovetic%2C+Dusan&rft.au=Xavier%2C+Joao&rft.au=Moura%2C+Jose+M.+F.&rft.date=2014-05-01&rft.issn=0018-9286&rft.eissn=1558-2523&rft.volume=59&rft.issue=5&rft.spage=1131&rft.epage=1146&rft_id=info:doi/10.1109%2FTAC.2014.2298712&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TAC_2014_2298712 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9286&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9286&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9286&client=summon |