Local linear convergence analysis of Primal–Dual splitting methods

In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice.     

Polypyrrole Shell@3D‐Ni Metal Core Structured Electrodes for High‐Performance Supercapacitors

Three‐dimensional (3D) nanometal films serving as current collectors have attracted much interest recently owing to their promising application in high‐performance supercapacitors. In the process of the electrochemical reaction, the 3D structure can provide a short diffusion path for fast ion transport, and the highly conductive nanometal may serve as a backbone for facile electron transfer. In this work, a novel polypyrrole (PPy) shell@3D‐Ni‐core composite is developed to enhance the electrochemical performance of conventional PPy. With the introduction of a Ni metal core, the as‐prepared material exhibits a high specific capacitance (726 F g^?1 at a charge/discharge rate of 1 A g^?1), good rate capability (a decay of 33 % in C_sp with charge/discharge rates increasing from 1 to 20 A g^?1), and high cycle stability (only a small decrease of 4.2 % in C_sp after 1000 cycles at a scan rate of 100 mV s^?1). Furthermore, an aqueous symmetric supercapacitor device is fabricated by using the as‐prepared composite as electrodes; the device demonstrates a high energy density (≈21.2 Wh kg^?1) and superior long‐term cycle ability (only 4.4 % and 18.6 % loss in C_sp after 2000 and 5000 cycles, respectively).     

Random copolymerization of macromonomers as a versatile strategy to synthesize mixed-graft block copolymers

The random copolymerization of norbornene-functionalized macromonomers was explored as a method of synthesizing mixed-graft block copolymers (mGBCPs). The copolymerization kinetics of a model system of polystyrene (PS) and poly(lactic acid) (PLA) macromonomers was first analyzed, revealing a gradient composition of side chains along the mGBCP backbone. The phase separation behavior of mGBCPs with PS and PLA side chains of various backbone lengths and side chain molar ratios was investigated, and increasing the backbone length was found to stabilize the phase-separated nanostructures. The graft architecture was also demonstrated to improve the processability of the mGBCP, compared to a linear counterpart. Investigations of mGBCPs comprised of polydimethylsiloxane and poly(ethylene oxide) side chains exemplified the diverse self-assembled morphologies, including a Frank-Kasper A15 phase, that can be obtained with mGBCPs synthesized by random copolymerization of macromonomers. Lastly, a ternary mGBCP was synthesized by the copolymerization of three macromonomers.