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Scientists use 'voting' and 'penalties' to overcome errors in quantum optimization（图）
voting penalties quantum optimization
2014/3/21
By tying quantum bits into voting blocks, scientists can create significant protection against decoherence.
Monte Carlo Portfolio Optimization for General Investor Risk-Return Objectives and Arbitrary Return Distributions: a Solution for Long-only Portfolios
Portfolio Optimization Optimisation Random Portfolio Monte Carlo Simplex
2010/10/21
We develop the idea of using Monte Carlo sampling of random portfolios to solve portfolio investment problems. In this first paper we explore the need for more general optimization tools, and conside...
Portfolio optimization in a defaults model under full/partial information
Optimal investment default time default intensity filtering
2010/10/19
In this paper, we consider a financial market with assets exposed to some risks inducing jumps in the asset prices, and which can still be traded after default times. We use a default-intensity model...
A note on evolutionary stochastic portfolio optimization and probabilistic constraints
note evolutionary stochastic portfolio optimization probabilistic constraints
2010/10/18
In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evol...
Classical mean-variance portfolio theory12 tells us how to construct a portfo-lio of assets which has the greatest expected return for a given level of return volatility. Utility theory then allows an...
The optimization of large portfolios displays an inherent instability to estimation error.
This poses a fundamental problem, because solutions that are not stable under sample
fluctuations may look ...
Portfolio optimization when expected stock returns are determined by exposure to risk
1/n strategy Black–Scholes model expected stock returns Markowitz’ problem portfolio optimization ranks
2010/11/1
It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time.The predo...