NP/CMP equivalence: a phenomenon hidden among sparsity models l_{0} minimization and l_{p} minimization for information processing

In this paper, we have proved that in every underdetermined linear system Ax = b, there corresponds a constant p*(A, b) > 0 such that every solution to the l p-norm minimization problem also solves the l0-norm minimization problem whenever 0 <; p <; p*(A, b). This phenomenon is named NP/CMP equivalence

PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices

Pipeline parallelism enables efficient training of Large Language Models (LLMs) on large-scale distributed accelerator clusters. Yet, pipeline bubbles during startup and tear-down reduce the utilization of accelerators. Although efficient pipeline schemes with micro-batching and bidirectional pipelines have been proposed to maximize utilization, a significant number of bubbles cannot be filled using synchronous forward and backward passes. To address this problem, we suggest that extra work be assigned to the bubbles to gain auxiliary benefits in LLM training. As an example in this direction, we propose PipeFisher, which assigns the work of K-FAC, a second-order optimization method based on the Fisher information matrix, to the bubbles to accelerate convergence. In Phase 1 pretraining of BERT-Base and -Large models, PipeFisher reduces the (simulated) training time to 50-75% compared to training with a first-order optimizer by greatly improving the accelerator utilization and benefiting from the improved convergence by K-FAC

The sparsity of underdetermined linear system via lp minimization for 0 < p < 1

The sparsity problems have attracted a great deal of attention in recent years, which aim to find the sparsest solution of a representation or an equation. In the paper, we mainly study the sparsity of underdetermined linear system via lp minimization for 0 0 such that the following conclusions hold when p < γ(A, b): (1) the problem (pp) generates sparser solution as the value of p decreases; (2) the sparsest optimal solution to the problem (pp) is unique under the sense of absolute value permutation; (3) let X1 and X2 be the sparsest optimal solution to the problems (pp1) and (pp2) , respectively, and let X1 not be the absolute value permutation of X2. Then there exist t1,t2 ε [p1,p2] such that X1 is the sparsest optimal solution to the problem (pt) (∀t ε [p1, t1]) and X2 is the sparsest optimal solution to the problem (pt) (∀t ε (t2, p2])

Pulse-duration dependence of high-order harmonic generation with coherent superposition state

We make a systematic study of high-order harmonic generation (HHG) in a He$^+$-like model ion when the initial states are prepared as a coherent superposition of the ground state and an excited state. It is found that, according to the degree of the ionization of the excited state, the laser intensity can be divided into three regimes in which HHG spectra exhibit different characteristics. The pulse-duration dependence of the HHG spectra in these regimes is studied. We also demonstrate evident advantages of using coherent superposition state to obtain high conversion efficiency. The conversion efficiency can be increased further if ultrashort laser pulses are employed