This paper presents a polynomial-time algorithm for recovering item values in the fractional knapsack problem using comparison queries, and provides a lower bound.

The paper establishes conditions for QUBO formulations of combinatorial optimization problems that guarantee valid binary and feasible local minimizers using gradient-based methods.

This paper presents an initialization framework and rounding procedure for interior point methods in linear programming, ensuring strongly polynomial time complexity.

The paper establishes a lower bound for the improvement of linear dependence on the restricted condition number in sparse convex optimization, using the Small-Set Expansion Hypothesis.

This paper derives new linear programming bounds for q-ary (r,δ)-locally recoverable codes, improving upon previous bounds and applying to both linear and nonlinear codes.

This paper measures the lower bound for the shortest program generating a sequence, proving a conservation law and providing a deterministic engine to recover generating programs for certain sequences…

The paper presents an algorithm for solving linear programming problems in the oracle model with improved time complexity.

This paper proposes a Hybrid Augmented Lagrangian (HyAL) method that integrates the constraint-handling strengths of the AL framework with the exploratory power of population-based search.

This paper presents a polynomial time algorithm for maximizing the number of satisfied constraints in a boolean constraint satisfaction problem, achieving a $(k/2^k)$-approximation.

This paper improves max-policy iteration by replacing mathematical optimization with value iteration and proposes min-policy iteration as an alternative to linear programming.

The paper addresses the failure of fixed-price inference in resource-constrained pricing controllers by developing a target-aware controller that tracks local densities and provides certified, shrinki…

The authors provide a deterministic algorithm for solving integer programs in standard form with improved running time.

This paper reviews models of inexact computational oracles in smooth continuous optimization and their implications for optimization algorithms.

This paper introduces Neural Certificate Pricing (NCP), an unsupervised learning framework that exploits the asymmetry between certifiable discrete structures and structural feasibility in combinatori…