Trotter errors in XY-mixers scale with individual constraint size and locality rather than total problem size, making them superior to X-mixers for local constraints but inferior for global ones, with a new mixer proposed for TSP-like constraints.
Portfolio selection.The Journal of Finance, 7(1):77–91
5 Pith papers cite this work. Polarity classification is still indexing.
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Polynomial elimination after weighting yields an explicit algebraic equation for the Pareto front in multi-objective polynomial optimization.
Agentic AI systems should be designed as marginal token allocators that balance benefit against cost, latency, and risk across their layers rather than as unit-priced text generators.
A PPO agent with reliability-shaped rewards and GARCH–EVT–t-copula scenarios matches or approaches NSGA-II on global equity indices across pre/COVID/post-COVID regimes under variance, CVaR, and EVaR.
Distributed systems in biology, economics, and computing optimize productivity by converging on maximum feasible heterogeneity, with environmental demands and communication topology setting the limits.
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Constraint Preserving XY-Mixers under Trotterized Adiabatic Evolution
Trotter errors in XY-mixers scale with individual constraint size and locality rather than total problem size, making them superior to X-mixers for local constraints but inferior for global ones, with a new mixer proposed for TSP-like constraints.
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Computing the Pareto Front by Polynomial Elimination, With an Application From System Identification
Polynomial elimination after weighting yields an explicit algebraic equation for the Pareto front in multi-objective polynomial optimization.
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Agentic AI Systems Should Be Designed as Marginal Token Allocators
Agentic AI systems should be designed as marginal token allocators that balance benefit against cost, latency, and risk across their layers rather than as unit-priced text generators.
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Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
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The Principle of Maximum Heterogeneity Optimises Productivity in Distributed Production Systems Across Biology, Economics, and Computing
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