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PLS (complexity)
Paradigms of Combinatorial Optimization
Solving Non-Boolean Satisfiability Problems with Stochastic Local Search
Random Θ(Log N)-Cnfs Are Hard for Cutting Planes
On Total Functions, Existence Theorems and Computational Complexity
Arxiv:1910.02319V2 [Cs.CV] 10 Nov 2020
A Tour of the Complexity Classes Between P and NP
Introduction to SAT History, Algorithms, Practical Considerations
Pure Nash Equilibria and PLS-Completeness∗
The PLS Regression Model: Algorithms and Application to Chemometric Data
Total NP Functions I: Complexity and Reducibility
Combinatorial Problems and Search
Evolving Combinatorial Problem Instances That Are Difficult to Solve
Random Cnfs Are Hard for Cutting Planes
NP,Conp and Funtion Problems Eurocg ’14, Ein-Gedi, Israel 1 / 18 a ”No”-Instance of a Problem in Conp Possesses a Short Proof of Being a ”No”-Instance
On the Cryptographic Hardness of Local Search∗
The Journey from NP to TFNP Hardness
An Overview of What We Can and Cannot Do with Local Search Petros Christopoulos, Vassilis Zissimopoulos
The Complexity of Gradient Descent: CLS = PPAD ∩ PLS John Fearnley Paul W
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NP-Hardness of `0 Minimization Problems: Revision and Extension to the Non-Negative Setting
A Polynomial-Time Algorithm for Unconstrained Binary Quadratic
Towards a Unified Complexity Theory of Total Functions
I. Introduction to NP Functions and Local Search
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Lecture 8 1 Introduction 2 Complexity Theory of Total Search Problems
Solving Traveling Salesman Problems with Heuristic Learning Approach Sim Kim Lau University of Wollongong
Arnold Beckmann and Faron Moller, on the Complexity of Parity Games
Lecture 3, Complexity of Equilibria in Congestion Games, May 11, 2015
Adventures in Monotone Complexity and TFNP
The Complexity Class Polynomial Local Search (PLS) and PLS-Complete Problems
The Complexity of Gradient Descent: CLS = PPAD ∩ PLS
The Complexity of Gradient Descent: CLS = PPAD ∩ PLS
A Note on the Complexity of P-Matrix LCP and Computillg an Equilibrium
Random Θ(Log N)-Cnfs Are Hard for Cutting Planes