Fluidized roasting reduction kinetics of low-grade pyrolusite coupling with pretreatment of stone coal

Based on the fluidized roasting reduction technology of low-grade pyrolusite coupling with pretreatment of stone coal, the manganese reduction efficiency was investigated and technical conditions were optimized. It is found that the optimum manganese reduction efficiency can be up to 98.97% under the conditions that the mass ratio of stone coal to pyrolusite is 3：1, the roasting temperature of stone coal is 1000℃, the roasting temperature of pyrolusite is 800℃, and the roasting time is 2 h. Other low-grade pyrolusite ores in China from Guangxi, Hunan, and Guizhou Provinces were tested and all these minerals responded well, giving -99% manganese reduction efficiency. Meanwhile, the reduction kinetic model has been established. It is confirmed that the reduction process is controlled by the interface chemical reaction. The apparent activation energy is 36.397 kJ/mol.

A Mini-Batch Proximal Stochastic Recursive Gradient Algorithm with Diagonal Barzilai–Borwein Stepsize

Many machine learning problems can be formulated as minimizing the sum of a function and a non-smooth regularization term.Proximal stochastic gradient methods are popular for solving such composite optimization problems.We propose a minibatch proximal stochastic recursive gradient algorithm SRG-DBB,which incorporates the diagonal Barzilai–Borwein(DBB)stepsize strategy to capture the local geometry of the problem.The linear convergence and complexity of SRG-DBB are analyzed for strongly convex functions.We further establish the linear convergence of SRGDBB under the non-strong convexity condition.Moreover,it is proved that SRG-DBB converges sublinearly in the convex case.Numerical experiments on standard data sets indicate that the performance of SRG-DBB is better than or comparable to the proximal stochastic recursive gradient algorithm with best-tuned scalar stepsizes or BB stepsizes.Furthermore,SRG-DBB is superior to some advanced mini-batch proximal stochastic gradient methods.

A ROBUST TRUST REGION ALGORITHM FOR SOLVING GENERAL NONLINEAR PROGRAMMING

Provides information on a study which presented a trust region approach for solving nonlinear constrained optimization. Algorithm of the trust region approach; Information on the global convergence of the algorithm; Numerical results of the study.