A single-phase, proximal path-following framework

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We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves ...

Knapsack and Subset Sum are fundamental NP-hard problems in combinatorial optimization. Recently there has been a growing interest in understanding the best possible pseudopolynomial running times for these problems with respect to various parameters. In t ...

Weighted flow time is a fundamental and very well-studied objective function in scheduling. In this paper, we study the setting of a single machine with preemptions. ...

This paper introduces a method for computing points satisfying the second-order necessary optimality conditions for nonconvex minimization problems subject to a closed and convex constraint set. The method comprises two independent steps corresponding to t ...

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We explore consequences of the Averaged Null Energy Condition (ANEC) for scaling dimensions Delta of operators in four-dimensional N = 1 superconformal field theories. We show that in many cases the ANEC bounds are stronger than the corresponding unitarity ...

We propose a new non-perturbative method for studying UV complete unitary quantum field theories (QFTs) with a mass gap in general number of spacetime dimensions. The method relies on unitarity formulated as positive semi-definiteness of the matrix of inne ...

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct algorithm for solving large SDP problems by economizing on both the storage an ...

Problem definition: Vehicle-to-grid increases the low utilization rate of privately owned electric vehicles by making their batteries available to electricity grids. We formulate a robust optimization problem that maximizes a vehicle owner’s expected profi ...

A single-phase, proximal path-following framework

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Computational Inverse Design of Surface-based Inflatables

Knapsack and Subset Sum with Small Items

A (2+epsilon)-Approximation Algorithm for Preemptive Weighted Flow Time on a Single Machine

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Implications of ANEC for SCFTs in four dimensions

Bootstrapping massive quantum field theories

Convergence without Convexity: Sampling, Optimization, and Games

Scalable Semidefinite Programming

Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints