👂🎴 🕸️
<
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>
Calculus
-
based
heuristics
''
like
Newton
'
s
method
or
gradient
descent
''
use
principles
of
calculus
to
find
solutions
to
complex
problems
.
They
involve
calculating
slopes
or
gradients
to
understand
how
a
function
changes
.
For
instance
''
gradient
descent
moves
towards
the
lowest
point
of
a
function
''
similar
to
finding
the
bottom
of
a
valley
-
this
point
is
often
the
best
solution
.
These
methods
are
powerful
for
optimizing
problems
''
like
in
machine
learning
or
economics
''
where
finding
the
most
efficient
point
is
crucial
.<
br
>
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>
<
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>
Newton
'
s
Method
is
a
calculus
-
based
technique
to
find
the
roots
of
a
function
''
where
the
function
equals
zero
.
It
starts
with
a
guess
and
repeatedly
applies
a
formula
to
get
closer
to
the
root
.
It
uses
the
function
'
s
slope
(
derivative
)
to
improve
each
guess
.
It
'
s
effective
for
smooth
''
continuous
functions
but
can
fail
for
functions
with
no
derivative
''
flat
slopes
''
or
multiple
roots
near
each
other
.
It
'
s
great
for
precise
''
math
-
heavy
problems
but
not
for
erratic
or
non
-
differentiable
functions
.<
br
>
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>
Gradient
Descent
is
a
method
used
to
find
the
minimum
of
a
function
.
Imagine
walking
downhill
towards
the
lowest
point
in
a
valley
—
that
'
s
what
this
method
does
mathematically
.
It
calculates
the
gradient
(
the
slope
)
of
the
function
and
takes
steps
in
the
direction
that
decreases
the
function
'
s
value
.
It
'
s
powerful
for
optimizing
in
machine
learning
and
economics
.
However
''
it
struggles
with
functions
having
many
valleys
(
local
minima
)
or
plateaus
''
and
might
not
find
the
absolute
lowest
point
(
global
minimum
).
The
3
-
SAT
(
3
-
Satisfiability
)
problem
is
a
classic
question
in
computer
science
and
mathematical
logic
.
It
'
s
a
specific
type
of
Boolean
satisfiability
problem
.
In
3
-
SAT
''
you
'
re
given
a
formula
composed
of
several
clauses
''
where
each
clause
is
a
disjunction
(
an
„
OR
“
operation
)
of
exactly
three
literals
(
variables
or
their
negations
).
The
challenge
is
to
determine
if
there
exists
an
assignment
of
truth
values
(
true
or
false
)
to
the
variables
that
makes
the
entire
formula
true
.
This
problem
is
known
for
being
<
strong
>
NP
-
complete
''
meaning
it
'
s
easy
to
check
a
solution
but
potentially
very
hard
to
find
one
strong
>''
especially
as
the
number
of
variables
increases
.
This
characteristic
makes
3
-
SAT
important
in
theoretical
computer
science
''
particularly
in
studies
related
to
computational
complexity
.
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