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Found 17 papers, 2 papers with code
Solving the Food-Energy-Water Nexus Problem via Intelligent Optimization Algorithms
The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread.
Liquidity Jump, Liquidity Diffusion, and Treatment on Wash Trading of Crypto Assets
We propose that the liquidity of an asset includes two components: liquidity jump and liquidity diffusion.
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity
This paper considers stochastic weakly convex optimization without the standard Lipschitz continuity assumption.
GBM-based Bregman Proximal Algorithms for Constrained Learning
In this paper, we adapt the GBM for constrained learning tasks within the framework of Bregman proximal algorithms.
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
In this paper, we propose the stochastic homogeneous second-order descent method (SHSODM) for stochastic functions enjoying gradient dominance property based on a recently proposed homogenization approach.
The Impacts of Registration Regime Implementation on IPO Pricing Efficiency
We find that the most efficient regulation regime in Chinese IPO pricing has four characteristics: 1) registration system, 2) no hard return caps nor trading curbs that restrict the initial return; 3) more specific listing rules for issuers, and 4) more stringent participation requirements for investors.
Liquidity Premium, Liquidity-Adjusted Return and Volatility, and Extreme Liquidity
We establish innovative liquidity premium measures, and construct liquidity-adjusted return and volatility to model assets with extreme liquidity, represented by a portfolio of selected crypto assets, and upon which we develop a set of liquidity-adjusted ARMA-GARCH/EGARCH models.
First-order methods for Stochastic Variational Inequality problems with Function Constraints
Second, to obtain the optimal operator complexity for smooth deterministic problems, we present a novel single-loop Adaptive Lagrangian Extrapolation~(\texttt{AdLagEx}) method that can adaptively search for and explicitly bound the Lagrange multipliers.
Stochastic Dimension-reduced Second-order Methods for Policy Optimization
In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods.
Efficient First-order Methods for Convex Optimization with Strongly Convex Function Constraints
Before our work, the best complexity bound was $\mathcal{O}(1/{\varepsilon})$, and it remains unclear how to improve this result by leveraging the strong convexity assumption.
The Impact of Regulation Regime Changes on ChiNext IPOs: Effects of 2013 and 2020 Reforms on Pricing and Overreaction
Based on our findings, we argue that the differences among the levels and determinants of initial return, monthly return (intrinsic value) and intramonth return (overreaction) in different time periods can be largely explained by regulation regime changes along two dimensions: 1) approval vs. registration and 2) listing day trading curbs and return limits.
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Second, motivated by the success of momentum stochastic gradient descent, we propose a new stochastic extrapolated model-based method, greatly extending the classic Polyak momentum technique to a wider class of stochastic algorithms for weakly convex optimization.
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
We also establish new convergence complexities to achieve an approximate KKT solution when the objective can be smooth/nonsmooth, deterministic/stochastic and convex/nonconvex with complexity that is on a par with gradient descent for unconstrained optimization problems in respective cases.
Artificial Intelligence BlockCloud (AIBC) Technical Whitepaper
The AIBC implements a two-consensus scheme to enforce upper-layer economic policies and achieve fundamental layer performance and robustness: the DPoEV incentive consensus on the application and resource layers, and the DABFT distributed consensus on the fundamental layer.
Efficiency of Coordinate Descent Methods For Structured Nonconvex Optimization
Third, we extend accelerated coordinate descent (ACD) to nonsmooth and nonconvex optimization to develop a novel randomized proximal DC algorithm whereby we solve the subproblem inexactly by ACD.
Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization
For large-scale and stochastic problems, we present a more practical proximal point method in which the approximate solutions of the subproblems are computed by the aforementioned ConEx method.
Optimal Adaptive and Accelerated Stochastic Gradient Descent
More specifically, we show that diagonal scaling, initially designed to improve vanilla stochastic gradient, can be incorporated into accelerated stochastic gradient descent to achieve the optimal rate of convergence for smooth stochastic optimization.
