鎶ュ憡棰樼洰錛歀inearized proximal algorithms with adaptive stepsizes for convex composite optimization with applications
銆€銆€鎶ュ憡浜猴細鏉庡啿錛堟禉姹熷ぇ瀛︼紝 鏁欐巿錛?鍗氬＋鐢熷甯堬級
銆€銆€鏃墮棿錛?018騫?2鏈?鏃ワ紙鏄熸湡鍥涳級 15:30鈥斺€?6:30
銆€銆€鍦扮偣錛氭牸鑷翠腑妤?00
銆€銆€鎶ュ憡鎽樿錛欼n this talk, we continue to study the problem of numerically solving convex composite optimizations. Linearized proximal algorithms (LPA) with adaptive stepsizes for solving the convex composite optimization problem are proposed. Local and/or global convergence properties of the proposed algorithms are explored, and their superlinear/quadratic convergence results are established under the assumptions of local weak sharp minima and the quasi-regularity condition. Our proposed algorithms, compared with the LPA with the constant stepsize, have the advantages of suiting for wider range of problems and of employing higher convergence rates. We apply the LPA with adaptive stepsizes to solve the wireless sensor network localization problem, and the numerical results show that the LPA with adaptive stepsizes can solve this problem more efficiently and stable than the LPA with the constant stepsize or other algorithms.
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鐞嗗闄?
2018騫?2鏈?鏃?/p>
