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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for optimization barrier

New method tackles bilevel optimization with polyhedral constraints.

problem Challenges in bilevel optimization with active-set changes and expensive Hessian inversions.
method Logarithmic barrier smoothing and proxy-gradient algorithm for differentiable approximation.
result Stationarity rates of O(K2/3)O(K^{-2/3}) in deterministic setting and O(K2/5)O(K^{-2/5}) under stochastic noise.

This paper is devoted to the pricing of Barrier options by optimal quadratic quantization method. From a known useful representation of the premium of barrier options one deduces an algorithm similar to one used to estimate nonlinear filter using quadratic optimal functional quantization. Some numerical tests are fulfi…

2010-12-05abs ↗pdf ↗

Research provides explicit NPV expressions for double barrier strategies.

problem Calculating expected NPVs of double barrier strategies for regular diffusions.
method Explicit expression using bivariate q-scale function with perturbation technique.
result Explicit expressions for expected NPVs are derived for certain cases.

Optimizes dividend control in a bankruptcy process using a special Levy process.

problem Optimizing dividend payouts in a bankruptcy process.
method Using a non-standard spectrally negative Levy process with endogenous regime switching.
result Optimal dividend control is of the barrier type and the optimal barrier can be identified.

We study a singular stochastic control problem faced by the owner of an insurance company that dynamically pays dividends and raises capital in the presence of the restriction that the surplus process must be above a given dividend payout barrier in order for dividend payments to be allowed. Bankruptcy occurs if the su…

2019-02-17abs ↗pdf ↗

This paper deals with a high-order accurate implicit finite-difference approach to the pricing of barrier options. In this way various types of barrier options are priced, including barrier options paying rebates, and options on dividend-paying-stocks. Moreover, the barriers may be monitored either continuously or disc…

2007-09-29abs ↗pdf ↗

A scalable framework optimizes multi-asset portfolios with constraints.

problem Optimizing multi-asset portfolios with inequality constraints.
method Integrates neural policies with Pontryagin's Maximum Principle, enforcing feasibility via log-barrier regularization.
result Recover KKT-optimal policies in high-dimensional problems without violating constraints.

IPMs struggle with hyperbolic spaces due to polynomially growing barrier parameters.

problem IPMs' efficiency is hindered in hyperbolic spaces.
method Analyzing the barrier parameter growth in hyperbolic and Hadamard spaces.
result The barrier parameter grows polynomially with the domain's diameter in hyperbolic spaces.

Short-term incentives lead to riskier trading strategies.

problem Optimal execution with performance barriers.
method Analyzes the impact of short-term performance incentives on trading behavior.
result Short-term incentives result in more aggressive but less risky trading strategies in the short term, but poorer performance over long periods.

RHMC improves sampling polytopes defined by inequalities with barriers.

problem Sampling polytopes defined by inequalities efficiently.
method Riemannian Hamiltonian Monte Carlo (RHMC) with a hybrid of Lewis weights and logarithmic barriers.
result RHMC achieves mixing rate of ildeO(m1/3n4/3) ilde O(m^{1/3}n^{4/3}) for polytopes defined by mm inequalities in Rn\R^n.

Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible set. For such challenging instances, we develop a new interior-point technique building on the Hessian-barrier algorithm recently introduced …

2019-11-04abs ↗pdf ↗

Unified framework improves robust causal inference, overcoming Gaussian barriers and optimization issues.

problem Improving robust causal inference in non-Gaussian settings.
method Combines gamma-Divergence, GNC, and Gatekeeper mechanism.
result Enhanced robustness and global optimization in causal effect estimation.

Paper proposes a method to find approximate SOSP for nonconvex conic optimization problems.

problem Finding approximate second-order stationary points in nonconvex conic optimization.
method Newton-CG based barrier method with complexity guarantees.
result Achieves iteration complexity of O(ε^(-3/2)) for finding (ε,√ε)-SOSP.

Neural networks' optimization dynamics are confined to a single basin despite connected basins in the loss landscape.

problem Neural networks' optimization dynamics are confined to a single basin despite connected basins in the loss landscape.
method Identifying entropic barriers arising from the interplay between curvature variations along low-loss paths and noise in optimization dynamics.
result Curvature-induced entropic forces bias noisy dynamics back toward the endpoints, explaining the confinement and connectivity of solutions.

New study reveals a polynomial penalty for adapting to unknown margin parameters in batched nonparametric bandits.

problem Adapting to an unknown margin parameter in batched nonparametric bandits.
method Introduces the regret inflation criterion and develops RoBIN algorithm to achieve optimal regret inflation.
result The optimal regret inflation grows polynomially with the horizon T, characterized by a convex optimization problem.

We revisit the dividend payment problem in the dual model of Avanzi et al. ([2], [1], and [3]). Using the fluctuation theory of spectrally positive Lévy processes, we give a short exposition in which we show the optimality of barrier strategies for all such Lévy processes. Moreover, we characterize the optimal barrier …

2012-11-30abs ↗pdf ↗

Post-training optimizes model performance beyond base model limits.

problem Optimizing sequence prediction models beyond the base model's support.
method Policy gradient (PG) and adaptive learning rate (LR) techniques.
result Post-training with PG can achieve near-optimal performance beyond the base model's support.

The paper calculates prices for multi-step barrier options under the Black-Scholes model.

problem Calculating prices for multi-step barrier options with varying barriers and time steps.
method Derives a general, explicit expression for option prices using the Black-Scholes model and a multi-step reflection principle.
result Derives a multi-step reflection principle that generalizes the reflection principle of Brownian motion.

Paper overcomes sample size barrier in reinforcement learning with generative models.

problem Sample efficiency in reinforcement learning with generative models.
method Developed two algorithms to certify minimax optimality of sample complexity.
result Achieved minimax-optimal guarantees for a wide range of sample sizes.

A time-dependent double-barrier option is a derivative security that delivers the terminal value φ(ST)φ(S_T) at expiry TT if neither of the continuous time-dependent barriers $b_\pm:[0,T]\to \RR_+$ have been hit during the time interval [0,T][0,T]. Using a probabilistic approach we obtain a decomposition of the barrier opti…

2008-09-10abs ↗pdf ↗

We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products We discuss the pricing metho…

2016-07-31abs ↗pdf ↗

Optimal reinsurance and dividend strategy for insurance companies in a finite time.

problem Maximizing dividends while managing risk in a finite time horizon.
method Dynamic control problem with Hamilton-Jacobi-Bellman equation, penalty approximation method.
result Smoothness of the value function and comparison principle for its gradient.

Efficient semi-analytic methods for pricing double barrier options with time-dependent parameters.

problem Pricing and calibration of double barrier options with time-dependent parameters.
method Two approaches: General Integral transform method and Heat Potential method.
result Semi-analytic techniques are more efficient for pricing double barrier options than traditional numerical methods.

Interior-point methods adapted for manifolds, achieving similar optimization results.

problem Optimizing on manifolds with self-concordant barriers.
method Generalization of self-concordance to Riemannian manifolds, path-following method analysis.
result Local quadratic convergence of Newton's method and standard complexity guarantees.

We provided an analytical representation of the price of a barrier option with one type of special moving barrier. We consider the case that risk free rate, dividend rate and stock volatility are time dependent. We get a pricing formula and put call parity for barrier option when the moving barrier has a special relati…

2013-03-06abs ↗pdf ↗

This work tackles Bayesian neural networks by addressing loss landscape symmetries.

problem Understanding and optimizing the loss landscape of Bayesian neural networks.
method The approach involves extending marginalized loss barrier formalism to BNNs, proposing a matching algorithm to search for linearly connected solutions using permutation matrices and combinatorial optimization.
result Nearly zero marginalized loss barriers for linearly connected solutions were found.

The paper optimizes dividend strategies for companies with assets and liabilities under solvency constraints.

problem Maximizing dividends while adhering to solvency requirements in the face of correlated asset and liability movements.
method Developed verification lemmas to show optimal barrier dividend strategies in two cases: with and without shareholder funding.
result Optimal dividend strategies are barrier-type, derived in closed form and illustrated.

Proximal policy optimization(PPO) has been proposed as a first-order optimization method for reinforcement learning. We should notice that an exterior penalty method is used in it. Often, the minimizers of the exterior penalty functions approach feasibility only in the limits as the penalty parameter grows increasingly…

2018-12-16abs ↗pdf ↗

In this paper we consider the optimal dividend problem for an insurance company whose risk process evolves as a spectrally negative Lévy process in the absence of dividend payments. The classical dividend problem for an insurance company consists in finding a dividend payment policy that maximizes the total expected di…

2007-02-28abs ↗pdf ↗

New principle reduces load imbalance in LLM serving systems, saving up to 52% energy.

problem Wasted computational power due to load imbalance in LLM serving systems.
method Developed a universal load-balancing principle for barrier-synchronized systems with non-migratable state.
result Proves worst-case theoretical guarantees for imbalance reduction and energy savings.