scholarly journals Nonnegativity constraints for structured complete systems

2015 ◽  
Vol 368 (8) ◽  
pp. 5783-5806 ◽  
Author(s):  
Alexander M. Powell ◽  
Anneliese H. Spaeth
Keyword(s):  
Nonnegativity Constraints

Improving Mean Variance Optimization through Sparse Hedging Restrictions

2015 ◽  
Vol 50 (6) ◽  
pp. 1415-1441 ◽  
Author(s):  
Shingo Goto ◽  
Yan Xu
Keyword(s):  
Covariance Matrix ◽  
Factor Model ◽  
Risk Minimization ◽  
Portfolio Risk ◽  
Finite Samples ◽  
Inverse Covariance Matrix ◽  
Out Of Sample ◽  
Nonnegativity Constraints ◽  
Mean Variance ◽  
Equal Weighting

AbstractIn portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

Graph Regularized Sparse Autoencoders with Nonnegativity Constraints

2019 ◽  
Vol 50 (1) ◽  
pp. 247-262
Author(s):  
Yueyang Teng ◽  
Yichao Liu ◽  
Jinliang Yang ◽  
Chen Li ◽  
Shouliang Qi ◽  
...  
Keyword(s):  
Nonnegativity Constraints

scholarly journals The Distributionally Robust Optimization Reformulation for Stochastic Complementarity Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Liyan Xu ◽  
Bo Yu ◽  
Wei Liu
Keyword(s):  
Robust Optimization ◽  
Linear Complementarity Problem ◽  
Complementarity Problem ◽  
Linear Complementarity ◽  
Uncertain Parameters ◽  
Distributionally Robust Optimization ◽  
Worst Case ◽  
Nonnegativity Constraints ◽  
Distributionally Robust ◽  
Stochastic Linear Complementarity Problem

We investigate the stochastic linear complementarity problem affinely affected by the uncertain parameters. Assuming that we have only limited information about the uncertain parameters, such as the first two moments or the first two moments as well as the support of the distribution, we formulate the stochastic linear complementarity problem as a distributionally robust optimization reformation which minimizes the worst case of an expected complementarity measure with nonnegativity constraints and a distributionally robust joint chance constraint representing that the probability of the linear mapping being nonnegative is not less than a given probability level. Applying the cone dual theory and S-procedure, we show that the distributionally robust counterpart of the uncertain complementarity problem can be conservatively approximated by the optimization with bilinear matrix inequalities. Preliminary numerical results show that a solution of our method is desirable.

Autocorrelation estimation using constrained iterative spectral deconvolution

Geophysics ◽  
1989 ◽  
Vol 54 (3) ◽  
pp. 381-391 ◽  
Author(s):  
Murali Ramaswamy ◽  
George E. Ioup
Keyword(s):  
Power Spectrum ◽  
Iterative Method ◽  
Maximum Entropy ◽  
Spectral Resolution ◽  
Spectral Estimate ◽  
Data Sequence ◽  
Spectral Deconvolution ◽  
Power Spectral ◽  
Nonnegativity Constraints ◽  
Entropy Technique

Computing an autocorrelation conventionally produces a biased estimate, especially for a short data sequence. Windowing the autocorrelation can remove the bias but at the expense of violating the nonnegativity of the corresponding power spectrum. Constrained iterative deconvolution provides a basis for improving an autocorrelation estimate by reducing the bias while guaranteeing nonnegative definiteness. The length of the autocorrelation is increased in order to satisfy the nonnegativity constraints on the power spectral estimate. The constraints can also have significant effects on small, poorly determined values of the autocorrelation. The technique is applied to synthetic and real examples to show the improvements in the autocorrelation and power spectrum which are possible. The method is reasonably stable in the presence of noise and it approximately preserves the area of the power spectrum. Comparison to the maximum entropy technique shows that the iterative method gives power spectral resolution which is sometimes better and sometimes not as good, but that there are cases for which it is the more desirable approach.

Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand

2001 ◽  
Vol 83 (3) ◽  
pp. 541-550 ◽  
Author(s):  
Amos Golan ◽  
Jeffrey M. Perloff ◽  
Edward Z. Shen
Keyword(s):  
Demand System ◽  
Meat Demand ◽  
Nonnegativity Constraints
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