Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems | Paper Citation Record

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This paper cites Towards strong nonapproximability results in the L ovasz- S chrijver hierarchy.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Towards strong nonapproximability results in the L ovasz- S chrijver hierarchy

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On non-approximability for quadratic programs

Reference 2

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems A fast and simple randomized parallel algorithm for the maximal independent set problem

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Proving integrality gaps without knowing the linear program

Reference 4

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Proving integrality gaps without knowing the linear program

Reference 5

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This paper cites Adversarially robust low dimensional representations.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Adversarially robust low dimensional representations

Reference 6

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On the usefulness of predicates

Reference 7

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This paper cites Topics in Banach space theory, volume 233 of Graduate Texts in Mathematics.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Topics in Banach space theory, volume 233 of Graduate Texts in Mathematics

Reference 8

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This paper cites Topics in Banach space theory , volume 233.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Topics in Banach space theory , volume 233

Reference 9

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This paper cites Quadratic forms on graphs.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Quadratic forms on graphs

Reference 10

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This paper cites Approximating the cut-norm via G rothendieck's inequality.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Approximating the cut-norm via G rothendieck's inequality

Reference 11

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Woodruff

Reference 12

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This paper cites Hypercontractivity, sum-of-squares proofs, and their applications.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Hypercontractivity, sum-of-squares proofs, and their applications

Reference 13

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Unresolved cited work

Reference 14

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This paper cites G rothendieck inequalities for semidefinite programs with rank constraint.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems G rothendieck inequalities for semidefinite programs with rank constraint

Reference 15

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This paper cites Weak decoupling, polynomial folds and approximate optimization over the sphere.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Weak decoupling, polynomial folds and approximate optimization over the sphere

Reference 16

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Deterministic and randomized polynomial-time approximation of radii

Reference 17

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This paper cites Sum-of-Squares Certificates for Maxima of Random Tensors on the Sphere.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Sum-of-Squares Certificates for Maxima of Random Tensors on the Sphere

Reference 18

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This paper cites SDP gaps from pairwise independence.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems SDP gaps from pairwise independence

Reference 19

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This paper cites Quantum de finetti theorems under local measurements with applications.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Quantum de finetti theorems under local measurements with applications

Reference 20

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This paper cites Estimating operator norms using covering nets.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Estimating operator norms using covering nets

Reference 21

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This paper cites A nearly tight sum-of-squares lower bound for the planted clique problem.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems A nearly tight sum-of-squares lower bound for the planted clique problem

Reference 22

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Hypercontractivity and its applications

Reference 23

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Kelner, and David Steurer

Reference 24

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This paper cites Dictionary learning and tensor decomposition via the sum-of-squares method.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Dictionary learning and tensor decomposition via the sum-of-squares method

Reference 25

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Kothari, and David Steurer

Reference 26

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This paper cites Separating the NP-Hardness of the Grothendieck Problem from the Little-Grothendieck Problem.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Separating the NP-Hardness of the Grothendieck Problem from the Little-Grothendieck Problem

Reference 27

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Noisy Tensor Completion via the Sum-of-Squares Hierarchy

Reference 28

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This paper cites The G rothendieck constant is strictly smaller than K rivine's bound.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The G rothendieck constant is strictly smaller than K rivine's bound

Reference 29

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Briët , O

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This paper cites Additive error guarantees for weighted low rank approximation.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Additive error guarantees for weighted low rank approximation

Reference 31

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Expansion in Proof Complexity

Reference 32

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Sum-of-squares proofs and the quest toward optimal algorithms

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This paper cites Invertibility of large submatrices with applications to the geometry of banach spaces and harmonic analysis.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Invertibility of large submatrices with applications to the geometry of banach spaces and harmonic analysis

Reference 34

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Random tensors and planted cliques

Reference 35

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Approximating matrix p-norms

Reference 36

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Approximating a finite metric by a small number of tree metrics

Reference 37

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The missing log in large deviations for triangle counts

Reference 38

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Lee, Prasad Raghavendra, and David Steurer

Reference 39

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Local global tradeoffs in metric embeddings

Reference 40

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Near-optimal algorithms for maximum constraint satisfaction problems

Reference 41

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This paper cites Integrality gaps for S herali- A dams relaxations.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Integrality gaps for S herali- A dams relaxations

Reference 42

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This paper cites Factoring weakly compact operators.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Factoring weakly compact operators

Reference 43

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Hopkins, Ankit Pensia, and Stefan Tiegel

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This paper cites The complexity of optimizing over a simplex, hypercube or sphere: a short survey.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The complexity of optimizing over a simplex, hypercube or sphere: a short survey

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Upper tails for triangles

Reference 46

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Tight upper tail bounds for cliques

Reference 47

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems A PTAS for the minimization of polynomials of fixed degree over the simplex

Reference 48

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Convergence analysis for Lasserre's measure--based hierarchy of upper bounds for polynomial optimization

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This paper cites An alternative proof of a PTAS for fixed-degree polynomial optimization over the simplex.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems An alternative proof of a PTAS for fixed-degree polynomial optimization over the simplex

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Decoupling: from dependence to independence

Reference 51

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Linear programming relaxations of maxcut

Reference 52

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This paper cites Improved sum-of-squares lower bounds for hidden clique and hidden submatrix problems.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Improved sum-of-squares lower bounds for hidden clique and hidden submatrix problems

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This paper cites Convergence of SDP hierarchies for polynomial optimization on the hypersphere.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Convergence of SDP hierarchies for polynomial optimization on the hypersphere

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Observation 46d760b9-03c4-4b9f-9b2b-0e3413aceb4e · outbound

This paper cites Relations between average case complexity and approximation complexity.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Relations between average case complexity and approximation complexity

Reference 55

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems A new approach to the planted clique problem

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The dimension of almost spherical sections of convex bodies

Reference 57

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This paper cites Block diagonally dominant matrices and generalizations of the gerschgorin circle theorem.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Block diagonally dominant matrices and generalizations of the gerschgorin circle theorem

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This paper cites Decomposing overcomplete 3rd order tensors using sum-of-squares algorithms.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Decomposing overcomplete 3rd order tensors using sum-of-squares algorithms

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This paper cites Estimating the matrix p q norm, 2023.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Estimating the matrix p q norm, 2023

Reference 60

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This paper cites R \'e sum \'e de la th \'e orie m \'e trique des produits tensoriels topologiques.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems R \'e sum \'e de la th \'e orie m \'e trique des produits tensoriels topologiques

Reference 61

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This paper cites Bypassing UGC from some optimal geometric inapproximability results.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Bypassing UGC from some optimal geometric inapproximability results

Reference 62

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This paper cites Goemans and David P.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Goemans and David P

Reference 63

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This paper cites The best constants in the khintchine inequality.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The best constants in the khintchine inequality

Reference 64

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This paper cites Clique is hard to approximate within n 1-&epsiv.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Clique is hard to approximate within n 1-&epsiv

Reference 65

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This paper cites Clique is hard to approximate within n^ 1-.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Clique is hard to approximate within n^ 1-

Reference 66

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This paper cites Some optimal inapproximability results.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Some optimal inapproximability results

Reference 67

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This paper cites Improved NP-Inapproximability for 2-Variable Linear Equations.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Improved NP-Inapproximability for 2-Variable Linear Equations

Reference 68

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This paper cites The power of sum-of-squares for detecting hidden structures.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The power of sum-of-squares for detecting hidden structures

Reference 69

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Observation c3ccf7b7-f49e-4eda-9c0b-6f7ff6b79c32 · outbound

This paper cites Approximation algorithms for homogeneous polynomial optimization with quadratic constraints.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Approximation algorithms for homogeneous polynomial optimization with quadratic constraints

Reference 70

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This paper cites Testing product states, quantum M erlin- A rthur games and tensor optimization.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Testing product states, quantum M erlin- A rthur games and tensor optimization

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:11.212640Z

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Observation 61722ad2-9bd6-4c9d-a39f-2181dc6e24a0 · outbound

This paper cites Limitations of semidefinite programs for separable states and entangled games.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Limitations of semidefinite programs for separable states and entangled games

Reference 72

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Observation 632bfb1c-8428-4c7d-bd49-c433d8ae7342 · outbound

This paper cites Tensor principal component analysis via sum-of-square proofs.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Tensor principal component analysis via sum-of-square proofs

Reference 73

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Observation 82aed1d1-74ad-4673-9bcc-f3e1b0dde89a · outbound

This paper cites On the advantage over a random assignment.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On the advantage over a random assignment

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:10.802616Z

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Observation 5b641452-bf95-4ee6-a730-55344433c2b9 · outbound

This paper cites Stable distributions, pseudorandom generators, embeddings, and data stream computation.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Stable distributions, pseudorandom generators, embeddings, and data stream computation

Reference 75

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Observation 7d7e077d-8e26-4808-a4d1-1a9e132be571 · outbound

This paper cites On the complexity of k-sat.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On the complexity of k-sat

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:10.444939Z

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Observation bfa52146-7c73-4bea-bc3f-9b02a4f62ec8 · outbound

This paper cites Upper tails for subgraph counts in random graphs.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Upper tails for subgraph counts in random graphs

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:10.232810Z

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Observation 7ba091fc-1bfa-4d3b-b8fb-3ed8e072e907 · outbound

This paper cites Hardness results for coloring 3-colorable 3-uniform hypergraphs.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Hardness results for coloring 3-colorable 3-uniform hypergraphs

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:10.139959Z

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Observation dac993b0-1e27-4b96-9097-c71f9d5df33d · outbound

This paper cites On the power of unique 2-prover 1-round games.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On the power of unique 2-prover 1-round games

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:10.095674Z

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Observation 7dfa738b-7509-4442-886d-a92e7a8789bd · outbound

This paper cites On the power of unique 2-prover 1-round games.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On the power of unique 2-prover 1-round games

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:10.050960Z

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Observation 8de9ad9a-23b7-474e-a234-0082deaf7798 · outbound

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Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Unresolved cited work

Reference 81

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Observation 1e77fd87-832d-4775-89b8-7fb1535cadc4 · outbound

This paper cites Measured descent: A new embedding method for finite metrics.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Measured descent: A new embedding method for finite metrics

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.976169Z

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Observation 98165419-e20f-4ee4-9811-62ba0dc8d322 · outbound

This paper cites Sum of squares lower bounds for refuting any CSP.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Sum of squares lower bounds for refuting any CSP

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.957865Z

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Observation 0a5c4828-e0b1-450a-bdfe-418e98bdaf5e · outbound

This paper cites Kothari, Ryuhei Mori, Ryan O'Donnell, and David Witmer.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Kothari, Ryuhei Mori, Ryan O'Donnell, and David Witmer

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.943816Z

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Observation 708286ca-8361-422d-be81-b8f6c90061d1 · outbound

This paper cites Approximating rectangles by juntas and weakly-exponential lower bounds for LP relaxations of CSP s.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Approximating rectangles by juntas and weakly-exponential lower bounds for LP relaxations of CSP s

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.929505Z

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Observation c8be0600-febb-410a-89ef-7706aa31749c · outbound

This paper cites Woodruff.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Woodruff

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.915018Z

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Observation c37b9081-5fa1-4614-b046-8c8744a5c1a5 · outbound

This paper cites Linear equations modulo 2 and the l\_1 diameter of convex bodies.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Linear equations modulo 2 and the l\_1 diameter of convex bodies

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.900129Z

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Observation 809d9b50-a4f9-451a-b080-7298a959d419 · outbound

This paper cites Grothendieck-type inequalities in combinatorial optimization.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Grothendieck-type inequalities in combinatorial optimization

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.886011Z

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Observation d5c7654f-36ad-4c91-8b7b-d22ee06612d1 · outbound

This paper cites The UGC hardness threshold of the L p G rothendieck problem.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The UGC hardness threshold of the L p G rothendieck problem

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.872418Z

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Observation 11d86b67-cefa-4e7f-bcb4-29f7beb52132 · outbound

This paper cites SDP gaps and UGC -hardness for Max-Cut-Gain.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems SDP gaps and UGC -hardness for Max-Cut-Gain

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.857806Z

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Observation cb900073-b914-4389-bc22-99c9655d442d · outbound

This paper cites On the approximability of digraph ordering.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On the approximability of digraph ordering

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.844554Z

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Observation e52d1028-12ed-492d-9c66-b2ef31ce134d · outbound

This paper cites Sur la constante de G rothendieck.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Sur la constante de G rothendieck

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.830425Z

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Observation 47d28efe-7709-429d-b782-37fd45b69384 · outbound

This paper cites Sdp integrality gaps with local _1 -embeddability.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Sdp integrality gaps with local _1 -embeddability

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.816905Z

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Observation 295924b8-2fec-42cb-8b2f-68f55a10114f · outbound

This paper cites Approximating CSP s using LP relaxation.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Approximating CSP s using LP relaxation

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.801665Z

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Observation dd07b0af-9c02-4792-b492-989277cf08ca · outbound

This paper cites The power of linear programming for general-valued CSP s.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems The power of linear programming for general-valued CSP s

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.785532Z

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Observation 6516133c-b0fe-4fe7-9185-c4c25a57dc4a · outbound

This paper cites A characterization of strong approximation resistance.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems A characterization of strong approximation resistance

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.770895Z

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Observation 72386657-e1f2-44bc-a877-aa62d12e46fc · outbound

This paper cites Divide and conquer martingales and the number of triangles in a random graph.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Divide and conquer martingales and the number of triangles in a random graph

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.756347Z

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Observation ce254e55-e83c-4a75-80ff-7f1d620c77d6 · outbound

This paper cites Isomorphic characterizations of inner product spaces by orthogonal series with vector valued coefficients.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Isomorphic characterizations of inner product spaces by orthogonal series with vector valued coefficients

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.742126Z

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Observation a1f9ee55-a372-4e35-9616-36572f45e380 · outbound

This paper cites On operators factorizable through l_p space.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems On operators factorizable through l_p space

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:37:09.728274Z

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Observation b03e7d38-534c-4c1a-81a9-95326e91c289 · outbound

This paper cites Moments, positive polynomials and their applications , volume 1.

Some Applications and Limitations of Convex Optimization Hierarchies for Discrete and Continuous Optimization Problems Moments, positive polynomials and their applications , volume 1

Reference 100

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