Projected gradient descentIn this paper we study the performance of the Projected Gradient Descent (PGD) algorithm for ℓ p -constrained least squares problems that arise in the framework of compressed sensing. Relying on the restricted isometry property, we provide convergence guarantees for this ...
The official Pytorch implementation of the paper named Unifying the factored and projected gradient descent for quantum state tomography, under review.AbstractReconstructing the state of many-body quantum systems is of fundamental importance in quantum information tasks, but extremely challenging due to the...
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed SensingThis paper considers reconstructing a spectrally sparse signal from a small number of randomly observed time-domain samples. The signal of interest is a linear combination of complex sinusoids at $R$...
This paper proposes a first-order algorithm that combines the well-known projected-projected gradient descent map with a rank reduction mechanism and generates a sequence in the variety whose accumulation points are Bouligand stationary. This algorithm compares favorably with the three other algorithms ...
3 ?4 6 way: Experiments We compare four methods discussed in this paper and refer to them in the following 1. mult: the multiplicative update method described in Section 2.1. 2. alspgrad: alternating non-negative least squares using the projected gradient method for each sub-problem (Section...
We can now frame our constrained learning problem as minimizing (1) over Π ⊂ H, that alternate between taking a gradient step in the general space H and projecting back down onto Π. This "lift-and- project" perspective motivates viewing our problem via the lens of mirror descent [40]...
Projected Newton Method for L1-Regularized Least Squares-:投影牛顿法的L1正则化最小二乘法—正则,一,L1,牛顿法,for,Least,least,牛顿吧,反馈意见 文档格式: .pdf 文档大小: 711.63K 文档页数: 6页 顶/踩数: 0/0 收藏人数: 0 评论次数: 0
The paper studies gradient descent algorithms for vehicle networks. Each vehicle within the network is modeled as a double integrator in the plane. For each individual vehicle, the control input enabling coordinated gradient descent consists of a gradient descent control ...
nonlinear equations/ accelerated projected steepest descent methodnonlinear inverse problemssparsity constraintsiterative algorithmiteration schemesprojected gradient methodThis paper is concerned with the construction of an iterative algorithm to solve nonlinear inverse problems with an lconstraint on x. One ...
In this paper, we present an Averaging Projection Stochastic Gradient Descent (APSGD) algorithm to solve the large-scale least squares problem. APSGD improves the Stochastic Gradient Descent (SGD) by using the constraint that the linear regression line passes through the mean point of all the ...