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.
This work explores the non-convex optimization in compressive learning and the performance of heuristics.
Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents in…
A new algorithm improves efficiency and robustness of heuristic optimization in simulation-based problems.
Heuristic algorithms such as simulated annealing, Concorde, and METIS are effective and widely used approaches to find solutions to combinatorial optimization problems. However, they are limited by the high sample complexity required to reach a reasonable solution from a cold-start. In this paper, we introduce a novel …
Heuristic weighting improves denoising score matching without requiring noise distribution assumptions.
RL improves combinatorial optimization by automating heuristic search.
Developed Forex trading heuristics with high profit potential.
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal…
New heuristic selects fewer assets for efficient portfolios, reducing costs.
Trading strategy mimics optimal control with simple heuristic.
We propose a general approach to modeling semi-supervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domain-specific heuristics. In ad…
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heurist…
Heuristic algorithm for portfolio optimization reduces solve times to milliseconds.
Deep RL learns 2-opt heuristics to improve TSP solutions.
Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained i…
Optimal prototypes found for challenging pathological geometries.
New clustering methods for binary data using combinatorial optimization.
Paper shows re-solving heuristics have constant regret for price-based revenue management.
Optimizes fund portfolio updates using linear programming and heuristic search.
Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuristics on routing problems has been steadily improving in recent years, but approaches based purely on machine learning are still outperformed…
We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). The proposed method can be used to specify ensembles of semi-supervised learning, as well as agreement constraints and entropic regularization constraints between these learners, and can be used to model both well-known h…
The paper develops algorithms for Boolean matrix factorization using IP and heuristics.
We consider the optimization of active extension portfolios. For this purpose, the optimization problem is rewritten as a stochastic programming model and solved using a clever multi-start local search heuristic, which turns out to provide stable solutions. The heuristic solutions are compared to optimization results o…
A new optimizer, MVO, improves nonlinear regression performance.
AdamQLR optimizes Adam with K-FAC heuristics, achieving comparable performance to tuned benchmarks.
Develops Heuristic Portfolio Optimization (HPO) as an information-restricted projection of Markowitz/tangency solution
L3Ms fine-tune LLMs with constraints for tailored applications.
A new model tracks indices without rebalancing, solving NP-hard problems.
The well-known Influence Maximization (IM) problem has been actively studied by researchers over the past decade, with emphasis on marketing and social networks. Existing research have obtained solutions to the IM problem by obtaining the influence spread and utilizing the property of submodularity. This paper is based…
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
This paper concerns a fundamental class of convex matrix optimization problems. It presents the first algorithm that uses optimal storage and provably computes a low-rank approximation of a solution. In particular, when all solutions have low rank, the algorithm converges to a solution. This algorithm, SketchyCGM, modi…
Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different vari- ations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two ke…
Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively i…
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based on attention …
A heuristic minimizes tardy jobs' total weight on single-machine scheduling.
Bayesian optimization has emerged as a strong candidate tool for global optimization of functions with expensive evaluation costs. However, due to the dynamic nature of research in Bayesian approaches, and the evolution of computing technology, using Bayesian optimization in a parallel computing environment remains a c…
A novel SVR parameter optimization method using GSA outperforms other meta-heuristics in stock market forecasting.
This paper optimizes UAV and FeICIC locations in a three-tier LTE-Advanced network.
This paper investigates Shampoo's heuristics and decouples preconditioner updates.
Model-based Bayesian Reinforcement Learning (BRL) allows a found formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL suffer from such a combinatorial explosion that a large body of work r…
Paper shows MCTS approximates policy optimization, proposing an improved variant.
New method optimizes share buyback contracts without optimal control's limitations.
New method decomposes corrupted data matrices into sparse and low-rank components.
Paper presents a probabilistic framework for diffusion synchronization.
PPO's gradients are heavy-tailed, affecting learning; a robust estimator improves performance.
Develops a power-calibrated framework for LLM watermarking, optimizing tradeoffs between detectability and distortion.
Adapting neural networks to guide program optimization for better classifiers.
Hybrid quantum-classical method optimizes financial index tracking.