Bayesian search optimizes exploration of feasible solutions under expensive constraints.
problem Identifying feasible solutions in computationally expensive constraint spaces.
method Bayesian models with an acquisition function for efficient exploration and exploitation.
result The proposed acquisition function improves the prediction of feasibility.
SFLS method finds feasible solutions faster with less data.
problem Efficiently solving SOECs with near-feasibility and near-optimality.
method SFLS method that emphasizes feasibility before convergence.
result SFLS maintains high-probability feasibility at each iteration.
The classical multi-set split feasibility problem seeks a point in the intersection of finitely many closed convex domain constraints, whose image under a linear mapping also lies in the intersection of finitely many closed convex range constraints. Split feasibility generalizes important inverse problems including con…
FMOPF generates diverse near-optimal power flow solutions.
problem Generating diverse near-optimal power flow solutions for risk quantification.
method Decouples compression from generation through latent flow matching and explicitly models load-state coupling.
result FMOPF provides the most effective Newton-Raphson warm starts and lowest tail risk.
Improved private learning of halfspaces with reduced sample complexity.
problem Private learning of halfspaces with reduced sample complexity.
method Iterative algorithm for solving linear feasibility problem, improving state-of-the-art results.
result Sample complexity reduced to d2.5⋅2log∗∣G∣, improving d2 factor. Rank-one measurements limit feasible sets for low-rank PSD matrices.
problem Feasibility of PSD matrices under rank-one measurements.
method Characterization of feasible sets for PSD matrices given rank-one projections.
result Radius of feasible sets determines singleton solution sets for low-rank matrices.
Neural network discovers exact solutions to QP with linear constraints.
problem Discovering exact solutions to Quadratic Programs (QP) with linear constraints using neural networks.
method Proposes a neural network modeling approach that analytically derives model parameters from problem coefficients, ensuring closed-form solutions without training.
result The closed-form NN model produces exact solutions for every critical region of the QP solution function, outperforming DNNs and commercial solvers in terms of optimality and feasibility.
New method improves feasibility of fitting Gaussian vectors to an ellipsoid.
problem Feasibility of fitting n Gaussian vectors to an ellipsoid boundary. method Improved concentration of Gram matrices using Bartl & Mendelson (2022) results.
result Feasibility of (P) with high probability when n≤d2/C. DC3 uses deep learning to solve hard-constrained optimization problems efficiently.
problem Hard constraints in optimization problems make classical solvers slow and infeasible.
method DC3 employs a differentiable procedure to enforce feasibility and unrolls corrections for inequality constraints.
result DC3 achieves near-optimal solutions while maintaining feasibility in both synthetic and real-world tasks.
Bayesian method approximates intractable stochastic programs with chance constraints.
problem Designing systems with stochastic constraints and chance constraints.
method Variational Bayesian approach to approximate posterior predictive integral.
result The solution set converges to the true solution set as the number of observations increases.
Paper addresses quadratic feasibility problems and their sample complexity.
problem Recovering complex vectors from quadratic measurements.
method Analyzes conditions for identifiability and explores optimization landscape.
result Gradient algorithms can converge to globally optimal solutions with high probability.
Machine learning speeds up train shunting feasibility determination.
problem Determining the feasibility of train shunting schedules.
method Deep Graph Convolutional Neural Network (DGCNN) to predict feasibility.
result Improves prediction accuracy and computational efficiency.
MI-GAN solves OPF with renewable uncertainty using model-informed layers.
problem Optimal Power Flow (OPF) under renewable uncertainty.
method Model-Informed Generative Adversarial Network (MI-GAN) framework with three layers.
result MI-GAN improves solution feasibility and optimality.
FOSC-X: An extended framework for extracting multiple optimal flat clusterings from hierarchical cluster trees
problem Extracting multiple optimal flat clusterings from hierarchical cluster trees
method Dynamic programming with lower and upper feasibility bounds
result Guaranteed optimal rankings of top-M solutions with linear-time complexity
Study proposes ATS algorithm to solve market clearing problem in Turkish day-ahead market.
problem Market clearing problem in Turkish day-ahead electricity market.
method Proposes an adaptive tabu search (ATS) algorithm to solve the problem, discretizing continuous search space and using adaptive radius.
result ATS algorithm performs better than heuristic decomposition method in most synthetic data sets.
Bayesian optimization tackles constrained high-dimensional problems with penalties and trust regions.
problem Constrained optimization in high-dimensional black-box settings with expensive evaluations and complex feasibility regions.
method Penalty formulation, surrogate model, trust region strategy, Expected Improvement acquisition function.
result The proposed Trust Region method identifies high-quality feasible solutions with fewer evaluations and maintains stable performance.
Paper introduces a method to generate physically feasible dynamics with physical priors.
problem Challenges in generating physically feasible dynamics under physical priors.
method Seamlessly incorporates physical priors into diffusion-based generative models.
result Efficient generation of physically realistic dynamics across various physical phenomena.
A new algorithm for solving constrained convex optimization problems efficiently.
problem Constrained convex optimization problems requiring high accuracy solutions.
method Second-Order Conditional Gradient Sliding (SOCGS) algorithm, using projection-free methods to solve quadratic subproblems inexactly.
result Converges quadratically in primal gap after a finite number of linearly convergent iterations.
Paper develops a fast method to find near-optimal power solutions.
problem Solving AC OPF on fast timescales for large networks.
method Leverages machine learning to map system loading to optimal generation values.
result Near-optimal and feasible solutions found on milliseconds timescales.
New method recovers sparse vectors from compressed, noisy data.
problem Recovering sparse vectors from compressed and noisy measurements.
method Non-convex quadratic programming exploiting prior magnitude information.
result More efficient support recovery with sufficient conditions for success.
In the present work we propose an original analytical model of coopetitive game. We try to apply this analytical model of coopetition - based on game theory and conceived at a macro level - to the Greek crisis, suggesting feasible solutions in a cooperative perspective for the divergent interests which drive the econom…
In this paper, we consider an online optimization process, where the objective functions are not convex (nor concave) but instead belong to a broad class of continuous submodular functions. We first propose a variant of the Frank-Wolfe algorithm that has access to the full gradient of the objective functions. We show t…
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical …
Paper addresses feasibility of counterfactual explanations in ML models, especially for critical domains.
problem Feasibility of counterfactual examples in ML models, especially in healthcare and finance.
method Uses partial structural causal models and modified variational autoencoder loss to generate counterfactuals that satisfy feasibility constraints.
result Generated counterfactuals better satisfy feasibility constraints than existing methods.
Unified framework for constrained diffusion models on nonconvex sets with efficient landing mechanism.
problem Efficiently modeling generative models under nonconvex constraints.
method Unified framework with overdamped and underdamped dynamics, landing mechanism.
result Significantly reduces computational cost while maintaining sample quality.
Analytic network learning simplifies multilayer network training without gradients.
problem Training multilayer networks efficiently and without gradient computation.
method Derive weight matrices via least squares error sense projections, exploiting feasible solutions.
result Analytic learning of multilayer networks without gradient computation.
Improved Frank-Wolfe algorithm for constrained convex optimization with nearest extreme point oracle.
problem Constrained smooth convex minimization with limited linear optimization oracle access.
method Frank-Wolfe algorithm with nearest extreme point oracle.
result Improved complexity bounds for specific feasible sets, including linear convergence for 0ext−−1 polytopes. New algorithm finds best feasible arm in grouped bandits.
problem Finding the best arm with all attributes above a threshold.
method Feasibility Constrained Successive Rejects (FCSR) algorithm.
result FCSR identifies the best feasible arm with optimal dependence on problem parameters.
Paper tackles energy efficiency in FL over wireless networks.
problem Energy efficient transmission and computation resource allocation for FL over wireless networks.
method Formulated as an optimization problem, iterative algorithm derived with closed-form solutions for time, bandwidth, power, and accuracy.
result Proposed algorithms reduce up to 59.5% energy consumption compared to conventional FL methods.
Develops a new framework for integrating satellite allocations in small portfolios.
problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outl…
Soft-Radial Projection solves gradient saturation in constrained deep learning.
problem Gradient saturation in deep learning models when integrating hard constraints.
method Introduces Soft-Radial Projection, a differentiable layer that maps predictions onto constraint boundaries without rank-deficient Jacobians.
result Improves convergence and solution quality over state-of-the-art methods.
The paper presents a practical method for evaluating investment projects using real options.
problem Evaluating investment projects under uncertainty and strategic risk management.
method Binomial trees and real options techniques for evaluating investment projects.
result The method can be used for most real options and introduces Project Value at Risk for feasibility.
Regularizes 3D inverse scattering with tangent-point energy for better solutions.
problem Ill-conditioned inverse obstacle scattering problems in 3D.
method Tikhonov regularization using tangent-point energy to penalize surface roughness and ensure well-posedness.
result Regularized solutions converge to true solution as noise level decreases.
Enhances OTA FL algorithms by defining inverse feasibility for linear models.
problem Improving security and privacy in over-the-air federated learning.
method Defines inverse feasibility as an upper bound on condition number, analyzes existing model, proposes new model.
result Proposes a new OTA FL model with enhanced characteristics.
Many engineering problems require identifying feasible domains under implicit constraints. One example is finding acceptable car body styling designs based on constraints like aesthetics and functionality. Current active-learning based methods learn feasible domains for bounded input spaces. However, we usually lack pr…
Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.
problem Optimizing investment portfolios with high inflation and bounded leverage constraints.
method Formulated an optimal control problem, established a closed-form solution, and developed a novel LFNN approach.
result The LFNN strategy outperforms a passive benchmark by about 200 bps with a high probability of success.
Shape-constrained symbolic regression improves model extrapolation with prior knowledge.
problem Improving model extrapolation with prior knowledge in symbolic regression.
method Shape-constrained symbolic regression using evolutionary algorithms with interval arithmetic.
result Models with shape constraints have improved extrapolation but lower accuracy on test sets.
New method for robust matrix completion with mixed data types.
problem Recovering a structured low rank matrix with mixed data types.
method Proposes a computationally feasible statistical approach with strong recovery guarantees for mixed data types.
result Strong recovery guarantees for low rank matrix completion with mixed data types.
The paper explores partial identifiability in nonnegative matrix factorization under specific conditions.
problem Identifying specific columns of the matrices in nonnegative matrix factorization.
method Mathematical rigor and geometric interpretation to analyze partial identifiability of columns in nonnegative matrix factorization.
result The partial uniqueness of a single column of C or S can be guaranteed under certain sparsity and algebraic conditions. In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problemati…
Bundle method solves low rank SDP problems without full matrix construction.
problem Solving semidefinite programming problems with low rank solutions.
method Applying bundle method to randomly sketch matrix optimization problems and using recent results on bundle methods.
result Algorithm produces solutions with low rank representation and convergence rates.
New algorithm exploits curvature of feasible sets for fast online convex optimization.
problem Online convex optimization with fast rates.
method Adapting FTL algorithm to curvature of feasible sets.
result Achieves logarithmic regret bound of O(ρlogT) in stochastic environments. Proposes a bond portfolio solution for managing interest rate risk.
problem Managing long-term assets and liabilities under interest rate risk.
method Proposes a bond portfolio solution based on ambiguity-averse preferences, accommodating various constraints and interest rate perturbations.
result Optimal portfolio can be computed as a simple generalized least squares problem, enhancing out-of-sample performance.
New algorithm tackles complex optimization problems with inexact and stochastic methods.
problem Solving complex optimization problems with inexact and stochastic methods.
method Developed ICGALP algorithm for composite minimization problems with inexact computations.
result Convergence of Lagrangian to an optimum and asymptotic feasibility of the affine constraint.
FACE generates actionable counterfactuals that are feasible and coherent with data.
problem Counterfactual explanations can be unachievable and offensive.
method FACE proposes a new approach to generate counterfactuals that are coherent with the data and feasible.
result FACE generates counterfactuals that are coherent with the data and feasible.
In this article we study an optimal stopping/optimal control problem which models the decision facing a risk-averse agent over when to sell an asset. The market is incomplete so that the asset exposure cannot be hedged. In addition to the decision over when to sell, the agent has to choose a control strategy which corr…
Comonotonic allocations are restored under certain constraints, improving risk-sharing.
problem Feasibility constraints can distort optimal risk-sharing allocations.
method Identified componentwise convex-order solidity as a sufficient condition to restore comonotonic allocations.
result Componentwise convex-order solidity ensures comonotonic improvements under feasible constraints.